PMC6021049

1 Table A. Linear mixed-effect model results for transformed minimum reaction times for all sprints in 2016. (...TRUNCATED)

On the apparent decrease in Olympic sprinter reaction times.(...TRUNCATED)

06-27-2018

Mirshams Shahshahani, Payam,Lipps, David B,Galecki, Andrzej T,Ashton-Miller, James A(...TRUNCATED)

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PMC10688325

Supplementary File 3: P-values of the Wilcoxon-Mann-Whitney tests assessing the null hypothesis that it is equally likely that a value chosen at random from one year is greater or less than a value chosen at random from another year’s population. Top 100 Table 1 Men’s 100m Table 2 Men‘s 110m hurdles Table 3 Men‘s 200m Table 4 Men‘s 400m Table 5 Men‘s 400m hurdles 2016 2017 2018 2019 2021 2017 1 2018 1 0.638283 2019 1 1 1 2021 1 0.309905 1 1 2022 1 0.00015 0.00196 0.003115 0.062573 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 0,714507 2021 0,097206 0,025 0,000356 0,003474 2022 0,003464 0,000552 4,56E-06 3,78E-05 0,726513 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 1 1 1 2022 0,052459 0,001119 0,046565 0,014442 0,047502 2016 2017 2018 2019 2021 2017 1 2018 0,572175 1 2019 1 1 1 2021 1 1 1 1 2022 0,052919 0,627806 1 0,078112 0,272402 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 1 1 1 2022 0,972897 1 1 0,388271 0,973264  Table 6 Women‘s 100m Table 7 Women‘s 100m hurdles Table 8 Women‘s 200m Table 9 Women‘s 400m Table 10 Women‘s 400m hurdles 2016 2017 2018 2019 2021 2017 1 2018 1 0,466331 2019 1 1 1 2021 0,03227 0,02574 0,139897 0,011156 2022 4,53E-07 4,06E-06 2,3E-06 4,28E-08 0,003582 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 0,004164 0,065499 0,129516 2022 0,746016 0,001377 0,023704 0,042453 1 2016 2017 2018 2019 2021 2017 1 2018 1 0,492091 2019 1 1 1 2021 1 0,265376 1 0,002601 2022 0,085241 0,000304 0,043264 2,27E-06 0,265376 2016 2017 2018 2019 2021 2017 1 2018 0,804407 0,371019 2019 1 0,702147 1 2021 1,35E-05 2,2E-07 0,001172 6,98E-05 2022 4,93E-05 5,75E-07 0,002364 0,000161 1 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 1 0,346209 0,375669 2022 1 0,608207 0,098102 0,080571 1  Top 20 Table 11 Men‘s 100m Table 112 Men‘s 110m hurdles Table 13 Men‘s 200m Table 14 Men‘s 400m Table 15 Men‘s 400m hurdles 2016 2017 2018 2019 2021 2017 1 2018 1 0,800315 2019 1 1 1 2021 0,972321 0,017573 0,297023 0,059996 2022 1 0,021544 0,33573 0,078011 1 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 0,303898 0,57473 0,003671 0,082601 2022 0,109341 0,290332 0,003215 0,062877 1 2016 2017 2018 2019 2021 2017 1 2018 1 0,062617 2019 1 0,175687 1 2021 1 0,685787 1 1 2022 0,175687 0,000813 0,232567 0,269417 0,154942 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 1 1 1 2022 1 1 1 1 1 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 0,407642 0,524398 0,53255 0,160764 2022 0,197812 0,338821 0,407642 0,160764 1  Table 16 Women‘s 100m Table 17 Women‘s 100m hurdles Table 18 Women‘s 200m Table 19 Women‘s 400m Table 20 Women‘s 400m hurdles 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 0,517847 0,006641 0,012547 2022 0,317904 0,018162 4,46E-05 0,004229 0,494987 2016 2017 2018 2019 2021 2017 0,711735 2018 1 1 2019 0,711735 1 0,699526 2021 0,045484 0,231794 0,114568 0,614191 2022 0,001485 0,001485 0,005646 0,014589 0,076741 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 0,012536 0,016489 0,003203 0,007309 2022 0,007309 0,007309 0,001137 0,005665 1 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 0,026829 0,011861 0,054195 0,010109 2022 0,467379 0,615339 0,757428 0,054195 1 2016 2017 2018 2019 2021 2017 1 2018 1 1 2019 1 1 1 2021 1 1 0,074017 0,574967 2022 0,103321 0,574967 0,005963 0,074017 1 (...TRUNCATED)

The potential impact of advanced footwear technology on the recent evolution of elite sprint performances.(...TRUNCATED)

11-27-2023

Mason, Joel,Niedziela, Dominik,Morin, Jean-Benoit,Groll, Andreas,Zech, Astrid(...TRUNCATED)

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PMC5325470

RESEARCH ARTICLE Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults Anne H. Y. Chu1*, Sheryl H. X. Ng1, Mahsa Paknezhad2, Alvaro Gauterin2, David Koh1,3, Michael S. Brown4, Falk Mu¨ller-Riemenschneider1,5 1 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore, 2 Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore, 3 PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, Brunei Darussalam, 4 Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada, 5 Institute of Social Medicine, Epidemiology and Health Economics, Charite´ University Medical Centre Berlin, Berlin, Germany * anne.chu@u.nus.edu, anne.chu.hy@gmail.com Abstract Introduction Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn Acti- Graph (wGT3X-BT) in free-living conditions. Methods 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categoriza- tions from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spear- man’s rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. Results The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all partici- pants, both genders, as well as daily physical activity categories (Spearman’s rho: 0.76– 0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4% (8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 1 / 13 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Chu AHY, Ng SHX, Paknezhad M, Gauterin A, Koh D, Brown MS, et al. (2017) Comparison of wrist-worn Fitbit Flex and waist- worn ActiGraph for measuring steps in free-living adults. PLoS ONE 12(2): e0172535. doi:10.1371/ journal.pone.0172535 Editor: Maciej Buchowski, Vanderbilt University, UNITED STATES Received: August 18, 2016 Accepted: February 6, 2017 Published: February 24, 2017 Copyright: © 2017 Chu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Due to ethical restrictions set by the National University of Singapore Institutional Review Board, study data cannot be made publicly available. Requests for data may be sent to Anne Chu (email: anne.chu@u. nus.edu), Falk Mu¨ller-Riemenschneider (email: ephmf@nus.edu.sg) or the National University of Singapore Institutional Review Board (email: irb@nus.edu.sg). Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66). Conclusions There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a chal- lenge that needs to be considered when using Fibit Flex in research and health promotion programs. Introduction New wearable technologies have helped raise individual self-awareness about physical activity behavior. Among all the functionalities that a range of wearable devices have, step counting is the most fundamental and consistently found feature. Step counts have been proposed as a health indicator for population studies [1], and even community-based health-promotion pro- grams [2]. The 10,000 steps/day guideline is one of the commonly used physical activity indices [3]. Various government/professional organizations around the world have used the 10,000 daily steps recommendation as an index of high physical activity level. This daily step-based rec- ommendation has been endorsed by the World Health Organization (WHO), National Heart Association of Australia, US Centers for Disease Control and Prevention, and American Heart Association to improve overall health. For healthy adults, it appears that this guideline is a real- istic estimate of an appropriate daily physical activity level [4, 5]. It was suggested that those achieving the goal of 10,000 steps per day were more likely to meet physical activity guidelines as compared to those with lower step counts [2]. Furthermore, health promotion programs that included a daily step goal were reportedly more successful in increasing physical activity than those without this component [6]. The use of step data (usually as steps/day) is a simple means of reflecting habitual physical activity pattern, and this approach has become acceptable to many researchers and practitioners [1, 6]. Moreover, walking activity has been reported as a prevalent form of leisure-time physical activity and a functional task in the daily lives [7]. Among all the accelerometers commonly used in research, the ActiGraph (Pensacola, FL, USA) is well-validated and has been extensively used for assessing physical activity under free- living conditions [8–11]; The ActiGraph accelerometers use algorithms to quantify and con- textualize the resultant acceleration signals of human motion. They have shown high accuracy for moderate-to-high walking speed stepping in the laboratory (compared to direct observa- tions, ICC: 0.72–0.99) and under free-living conditions (compared to the Yamax Digiwalker, ICC: 0.90) [12]. The ActiGraph has been used in large-scale epidemiological studies such as the US National Health and Nutrition Examination Survey (NHANES) [13], and the Women’s Health Study (WHS) [14]. Recently, consumer-based activity trackers (e.g. Fitbit, Jawbone UP, LUMOback, Nike + Fuelband, Omron Walking Style Pro pedometer, etc.) and in-built accelerometers in smart- phones have become increasingly popular [15, 16]. It was forecasted that the smart wearables market could reach 170 million units by 2017 [17]. Fitbit (San Francisco, CA, USA) is one of the most commonly used brands amongst the consumer-based activity trackers. As of 2015, Fitbit had reached 9.5 million active users [18]. Among their products, the wrist-worn Fitbit Flex has become popular in recent years either for aesthetic reasons or wearing comfort. The Fitbit Flex is sleek and displays only LED with a tap screen. Users are able to monitor and Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 2 / 13 Funding: This research was supported by a grant WBS: R-608-000-117-646 from the National University of Singapore. Competing interests: The authors have declared that no competing interests exist. access data on the number of steps, sleep quality, and other personal metrics through the Fitbit dashboard. This could be useful for targeted physical activity interventions designed to achieve healthy behaviors. It was suggested that wrist-worn accelerometers allowed for monitoring of low-intensity activities, and were associated with considerable increases in wearing compliance and data quality [19]. A number of studies have validated wireless consumer-based monitors of different brands in measuring step counts and energy expenditure [16, 20–23]. A recent systematic review con- cluded high validity for the Fitbit Classic, One and Zip compared to accelerometry-based step counts (particularly in laboratory settings) [24]. It was further highlighted that more field- based studies are needed. Evaluation of the trackers in assessing free-living physical activity (non-controlled environment outside a lab setting) is particularly important, as the results are more likely to reflect usual day-to-day activities. To date, sample sizes of studies on the Fitbit Flex validity under free-living conditions have been relatively small (ranging from 14 to 25 par- ticipants) and based on young adults [16, 25–27]. Of note, one similar study was limited by a small sample size of one adult only [28]. However, despite the high correlation between activity trackers, these studies generally showed that Fitbit Flex has measurement limitations regarding the overestimation and underestimation of activity levels compared with the reference device, depending on different study settings and types of activity [26, 27]. Given these considerations and highlighted gaps, this study aimed to make standardized comparisons based on step counts from the consumer-oriented Fitbit Flex and the research- grade ActiGraph wGT3X-BT. Differences in levels and types of physical activity between males and females have been reported [29, 30]. It was reported that more males than females tended to practise sports (e.g. soccer, basketball, etc.), whereas females were more likely to engage in yoga, dancing, aerobics, etc. [31]. Because these differences may influence their accu- racy in measurement, we further performed gender specific analysis. Hence, the objectives of this study were: 1. To compare free-living steps/day recorded by the Fitbit Flex and the ActiGraph wGT3X-BT accelerometers in all participants, by each step-defined daily physical activity category and gender. 2. To compare the agreement between devices in classifying participants’ step-defined daily physical activity categories. Materials and methods Study design and participants This was a cross-sectional study. The present study was a part of a previously published study [32], whereby a convenience sample of 107 employees who completed both ActiGraph and Fit- bit Flex measures were included. Participants from a large public University and a hospital in Singapore were recruited between February 2014 and June 2014. Individuals were residing in Singapore and were of various ethnicities (Chinese, Malay, Indian and others). Participants were invited to take part in this study through mass e-mailing. Individuals who indicated inter- est were approached and interviewed by the researcher. The inclusion criteria were: 1. Males and females aged 21 to 65 years 2. Either students or working adults 3. Absence of physical disabilities or illness that would create abnormal gait patterns. Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 3 / 13 The study was approved by the National University of Singapore Institutional Review Board (NUS-IRB Ref No.: B-14-021). Participants provided their written informed consent to participate in this study. Procedure The goals and procedures of the study were explained to each participant by the researcher via face-to-face interview. Participants’ information on gender, age, education level, height and weight were self-reported. Instructions were given to the participants by trained personnel on how to put on a wrist-worn Fitbit Flex and a waist-worn ActiGraph concurrently for 7 days. Instruction manuals on the proper use of the ActiGraph and Fitbit Flex were also given to par- ticipants for additional guidance. Participants were instructed that the devices had to be worn for at least 10 hours/day, and could be removed at night depending on their comfort level. They were asked to complete a daily time sheet to record each wearing day when both devices were worn while maintaining their normal activities. Information required on the time sheet comprised of the dates they started and stopped wearing the devices. ActiGraph wGT3X-BT The ActiGraph™ wGT3X-BT monitor (ActiGraph, LLC, Pensacola, Florida, USA) is a triaxial accelerometer (Dimensions: 4.6cm x 3.3cm x 1.5cm; weight: 19 grams) worn on the waist using an elastic belt to secure above the right hip bone for quantifying the amount and fre- quency of human movements. The monitor was initialized at a sample rate of 30Hz to record activities for free-living conditions. Participants were instructed to wear the ActiGraph for 7-day. They were allowed to remove the ActiGraph only while bathing or immersing the body in water. ActiGraph data were downloaded using ActiLife 6 software (ActiGraph, LLC, Pensa- cola, FL, USA) by the researchers upon collection of the devices. Downloaded data were inte- grated into 60-sec epochs. Fitbit Flex Fitbit FlexTM (Dimensions: 22.2cm x 6.0cm x 6.0cm; weight: 100 grams) is a wrist-worn wear- able wireless sensor with a triaxial accelerometer that records physical activity throughout the day. It can sync with a smartphone application/computer. Participants were instructed to wear the Fitbit Flex on their non-dominant wrist, for the same duration as the ActiGraph (up to 7-day) concurrently. In general, Fitbit Flex requires the creation of individual user accounts to download stored data using a Web-based software application. However, for the purpose of our study, anonymous user accounts were created by the study team which could only be accessed by the researchers. Steps data were therefore stored on the devices, and the minute- by-minute Fitbit Flex data were downloaded at the end of each participant’s wearing period by the study team. Data reduction For wear time validation, because the ActiGraph accelerometer is an established device to mea- sure physical activity with many validation studies determining their accuracy [33, 34], valid wear time determined by the ActiGraph was regarded as the reference. A detailed description of the procedures on ActiGraph wear time validation and removal of sleep time can be found elsewhere [32]. Then, a valid day was defined as having an accumulation of 1500 steps/day with 10 hours/day restricted only to common wear time based on both ActiGraph and Fitbit Flex. The 1500 steps/day criterion was based on a previous research conducted by Tudor- Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 4 / 13 Locke et al. comparing accelerometers positioned at different locations under free-living con- ditions [35]. All participants with 4 valid days of data were included in the analysis. Addi- tionally, wear time was also verified based on the daily time sheets. Statistical analysis All statistical procedures were performed using SPSS software (version 20.0). The significance level was set at P<0.05. Descriptive characteristics were presented as mean (standard devia- tion; SD) or median (interquartile range; IQR). Shapiro-Wilk test was used to determine whether the data was normally distributed. Differences in the characteristics between genders were detected by non-parametric tests. Mann-Whitney U test (for continuous variables), chi- squared test (for categorical variables) and Fisher’s exact test (for categorical variables with cells having an expected frequency of five or less) were used. Analyses of the relationship between ActiGraph and Fitbit Flex were performed across: all participants, by each category of step-defined daily physical activity, and gender. Because there could be potential within-subject variations, comparison of step counts for the magnitude of relationship between the two devices was done on a day-to-day basis. Spearman’s correlation coefficient (rho) and intraclass correlation coefficient (ICC) were used to assess correlation and agreement, respectively in steps between ActiGraph and Fitbit Flex. An ICC value of 0.75 implied excellent, 0.60–0.74 good, 0.40–0.59 fair and <0.40 poor agreement [36]. Median of absolute percentage error (MAPE) between devices was calculated: (absolute error/ observed steps) × 100%. The difference in MAPE by each category of step-defined daily physi- cal activity and gender was compared using Mann-Whitney U test. ActiGraph derived steps/ day was used to classify two step-defined activity categories for the assessments of Spearman’s rho and ICC. The classification of days into two step-defined activity categories was adapted based on previous studies: valid days with a cumulative of 10,000 steps/day were considered as active days, and <10,000 steps/day were inactive days [5, 37, 38]. As for the Bland-Altman analysis, a non-parametric approach was adopted since the differences between the two devices were non-normally distributed. Bland-Altman plots were presented as median, 10th and 90th percentiles to display variance around differences between two devices. Proportion of agree- ment in achievement of 10,000 steps per day produced by ActiGraph and Fitbit Flex was assessed using Kappa. Results Out of 107 recruited participants, 104 were included because they met the wear time criteria and provided 682 days of data. Table 1 shows participants’ sociodemographic characteristics of the study. Participants had a median age of 31.0 years (IQR: 26.0–42.8), predominantly female (66.3%), and had a university degree (74.0%). On average, 6.6 valid wear days were recorded per participant and there was no significant difference between males and females. The Acti- Graph and Fitbit Flex steps were significantly higher in males than females (P = 0.03 and 0.01 for ActiGraph and Fitbit Flex, respectively). Fitbit Flex recorded a significantly higher (P < 0.001) number of daily step counts than that from the ActiGraph across all participants, by gender and each category of step-defined daily physical activity (Table 2). Males reflect significantly higher daily step counts from Fitbit Flex (P = 0.01) and ActiGraph (P = 0.028) compared to females. The magnitude of the correlation and agreement in step counts between ActiGraph and Fit- bit Flex were assessed (Table 2). Good to excellent significant positive correlations and agree- ment were shown in all participants, by gender and category of step-defined daily physical activity. Table 3 shows the number of days that were misclassified as active or inactive Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 5 / 13 according to the Fitbit Flex. The proportion of overall agreement of devices in classifying days as active or inactive was estimated, reporting a kappa of 0.66, indicating a moderate agreement (Table 3). Fig 1 shows the MAPE in number of steps between the two devices. Significant differences in the MAPE of step counts were found between devices across step-defined physical activity categories (P<0.001), but not for gender (P = 0.17). Figs 2 and 3A–3D present Bland-Altman plots on the median of differences, and the 10th and 90th percentiles between steps/day obtained from Fitbit Flex and ActiGraph. The bias (median difference) is 1300 steps/day for all participants. In general, the Fitbit Flex overesti- mated steps/day relative to ActiGraph (median differences range: 1166–1509 steps/day by gen- der and 1280–1312 by step-defined physical activity categories). Discussion This study focused on the direct comparison of steps obtained from the Fitbit Flex and Acti- Graph. The results show positive correlations and agreement in step counts of free-living adults as measured by the waist-worn ActiGraph and wrist-worn Fitbit Flex activity monitors. At the same time, overestimation of step counts and classification as active days by Fitbit Flex were found. This may have important public health implications if consumers or participants of health promotion programs are identified as being active when in fact they are not. Table 1. Characteristics of study population. All (n = 104) Males (n = 35) Females (n = 69) P-valuea Age (Med; IQR) 31.0; 26.0–42.8 33.0; 27.0–50.0 30.0; 25.5–40.5 0.05 Height, cm (Med; IQR) 163.0; 157.0–169.8 170.0; 168.0–175.0 160.0; 155.0–163.0 <0.001 Weight, kg (Med; IQR) 60.0; 53.0–69.9 65.0; 60.0–80.0 56.6; 50.0–66.0 <0.001 BMI (Med; IQR) 22.6; 20.3–25.5 23.1; 20.8–25.8 22.1; 20.2–25.1 0.3 Education, n (%) 0.01 Secondary 7 (6.8) 0 (0) 7 (10.2) Technical school/diploma 20 (19.2) 3 (8.6) 17 (24.6) University 77 (74.0) 32 (91.4) 45 (65.2) Organization, n (%) 0.51 Public university 70 (67.3) 24 (68.6) 46 (66.7) University hospital 34 (32.7) 11 (31.4) 23 (33.3) 0.92 Valid wearing day/week (M±SD) 6.6 ± 0.9 6.6 ± 1.0 6.5 ± 0.9 BMI, body mass index; IQR, interquartile range; M, mean; Med, median; SD standard deviation. a Test of significant difference between males and females. doi:10.1371/journal.pone.0172535.t001 Table 2. Comparison, relative agreement and median of absolute error in step counts between ActiGraph and Fitbit Flex: all participants, by gen- der and category of step-defined daily physical activity. Step count/day All (682 days) Males (229 days) Females (453 days) Inactive (426 days) Active (256 days) Fitbit Flex (Med; IQR) 10193; 7490–12898a 11030; 7604–14838a 9992; 7397–12509a 8235; 6267–10003a 14075; 11948–16864a ActiGraph (Med; IQR) 8812; 6152–11471a 9409; 6268–12897a 8599; 6053–11118a 6856; 4982–8465a 12716; 11112–14505a Spearman’s rho 0.89* 0.91* 0.87* 0.76* 0.76* ICC (95% CI) 0.85 (0.58–0.93) 0.87 (0.56–0.94) 0.83 (0.56–0.92) 0.73 (0.68–0.77) 0.82 (0.77–0.85) CI, confidence interval; IQR, interquartile range. a ActiGraph and Fitbit Flex estimates are significantly different (P < 0.05). * P < 0.01 doi:10.1371/journal.pone.0172535.t002 Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 6 / 13 Recently, a number of studies have investigated the accuracy of various consumer-based physical activity trackers, recognizing the role they may play in physical activity promotion. For instance, Case et al. [16], Storm et al. [20], and Diaz et al. [21] have validated consumer wearables for measuring steps. However, to date very few studies have investigated the accu- racy of these monitors under free-living conditions [24]. This is highly important because the accuracy of devices may differ considerably in day-to-day life as compared to under highly controlled and short protocols of activities. Recently, several studies have been conducted with regard to this important research question [25–27]. Dierker et al. [25] assessed the validity of Fitbit Flex among 17 college-aged adults and found that although the steps measured by Fitbit Flex (9596 ± 2361 steps) were higher than the ActiGraph GT3X+ (7766 ± 2388 steps), the dif- ference was not statistically significant (P = 0.052). However, the authors instructed the partici- pants to remove the devices while they were exercising over the 7-day monitoring period; hence it is possible that not all free-living movements have been captured as in the present Table 3. Agreement between ActiGraph and Fitbit Flex for categorizing step-defined daily physical activity. No. of days (%)a ActiGraph Fitbit Flex Inactive Active Inactive 320 (46.9) 11 (1.6) Active 106 (15.5) 245 (35.9) Total 426 (62.5) 256 (37.5) Kappa (95% CI) 0.66 (0.61–0.71) a Physical activity categories are based on ActiGraph daily step counts: inactive <10,000 steps/day and active 10,000 steps/day [5]. doi:10.1371/journal.pone.0172535.t003 Fig 1. MAPE (%) between ActiGraph and Fitbit Flex. Error bars indicate IQR of MAPE. MAPE, median absolute percentage error. doi:10.1371/journal.pone.0172535.g001 Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 7 / 13 study. In another study by Dominick et al. [26], the Fitbit Flex registered a total of 10286 ± 3760 free-living steps/day as compared to the ActiGraph of 9639 ± 3456 steps/day (albeit no significant difference was found between devices) among 19 participants. In contrast, Sus- hames et al. [27] reported a larger absolute difference of over 3000 steps (47.0%) in free-living steps between Fitbit Flex and ActiGraph among 25 adults, of which the Fitbit Flex has underes- timated step counts. The reason for this underestimation from Fitbit Flex is unclear, but it could be related to the variability in participants’ movements or undercounting of steps by the Fitbit Flex. Different study settings and reference methods could contribute to the discrepancies in out- comes. Kooiman et al. [39] assessed the validity of Fitbit Flex over 1 day in a smaller sample of free-living adults and found high agreements in steps with the activPAL. They found a notice- ably smaller mean absolute percentage difference of 3.7% against the activPAL [39]. In accor- dance with our findings, another recent study comparing Fitbit Flex and ActiGraph on 48 cardiac patients (mean age: 65.5 years), in which high correlations and a difference in step counts of 1038 steps/day in the total population over 4 days of monitoring period were reported. Thus, comparing findings among different populations can provide an implication Fig 2. Bland-Altman plot of differences between waist-worn ActiGraph and wrist-worn Fitbit Flex against the mean according to all participants. The solid line represents median of the differences between devices, dotted lines are 10th and 90th percentiles of the differences. doi:10.1371/journal.pone.0172535.g002 Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 8 / 13 of how reproducible and valid this device is. It was also noted that the overestimation in step counts by the Fitbit Flex in this study resulted in a considerable misclassification of days as being active, which may have important public health implications. As shown in our analysis, the differences in steps between Fitbit Flex and ActiGraph were larger on inactive days as com- pared to active days. Hypothetically, as most lifestyle activities include movements at the wrist, people might have performed movements such as hand waving that could be identified as potential false pos- itive events/steps by Fitbit Flex. It was apparent that wrist-movements could reflect arm/ Fig 3. Bland-Altman plots of differences between waist-worn ActiGraph and wrist-worn Fitbit Flex against the mean according to: (A) Males, (B) Females, (C) Inactive days, and (D) Active days. The solid lines represent median of the differences between devices, dotted lines are 10th and 90th percentiles of the differences. doi:10.1371/journal.pone.0172535.g003 Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 9 / 13 forearm motions with a relatively small mass (while sitting), or they could be classified as step counts (while walking or running) [40]. Tudor-Locke et al. [35] found a large difference even using the same ActiGraph device placed between different attachment sites. They further reported that the difference between mean steps from the wrist and waist was 2558 steps under free-living conditions, with a higher average step counts on the wrist [35]. In line with this, Hilderbrand et al. [41] found a 200% higher step activity from the wrist-worn GENEActiv than the waist-worn ActiGraph in some adults. These observations suggest room for further progress, since recent studies reported using wrist-worn monitors resulted in improved wear- ing compliance due to comfort issues and without having the need to remove them intermit- tently [42]. Ultimately, prolonged wear time would improve data quality as the issue of missing data due to non-compliance could be minimized. Strengths Despite the growing body of evidence, this study expands substantially on previous studies. Most importantly, as highlighted earlier, the comparison of the devices was done under free-living conditions for estimation of unstructured lifestyle activities. Secondly, the relationship between these devices were assessed for 7-day of wearing protocol. Thirdly, this study was conducted among a relatively large sample of adults. Fourthly, the performance of the devices was compared across different subgroups (males vs. females and step-defined physical activity categories). Limitations This study may have limited generalizability as participants were predominantly females, rela- tively young and healthy. Furthermore, the use of ActiGraph as the reference instrument has its drawbacks. It is possible that the difference in steps between devices could be attributable to not only the Fitbit Flex, but also the ActiGraph, which is not the gold standard for measuring step counts [43]. However, the ActiGraph has been shown to be a valid tool to assess step count (as compared with the Omron pedometer and Yamax Digiwalker [11, 12]), and it is practical for use in epidemiological studies [44]. Careful consideration should also be given to the effects of movement artefact and signal noise due to the use of devices that are not attached directly to the skin (i.e. Fitbit Flex worn on a wrist-band and ActiGraph on a waist-belt), which might have affected the devices’ functionality to accurately measure step count. Being limited to only step count data, there was no indication as to whether the activities performed were of light-, moderate- or vigorous-intensity level. In general, step counts from accelerome- ters of different attachment sites (i.e. wrist- and waist-worn) might not be ideal for a direct comparison; nonetheless, results of this study were more likely to reflect the performances of these devices in real-world practice. Conclusions Positive correlation and agreement in step counts were found between wrist-worn Fitbit Flex and waist-worn ActiGraph in free living adults, which is consistent with the existing evidence mainly from laboratory studies. However, a considerable overestimation of Fitbit Flex was noted, which resulted in substantial misclassification by Fitbit Flex when applying common step count recommendations. This can have important practical implications for the use of these devices by researchers, practitioners and health promoters, which often use the achieve- ment of certain step count goals or increases in step counts as desired outcomes. Evidence pre- sented in this paper adds to the existing literature on the validity of consumer devices for physical activity monitoring and these cautionary limitations should be considered in the design of study data collection and health promotion strategies. Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 10 / 13 Acknowledgments We thank our colleagues and participants for their involvement in this study. Author Contributions Conceptualization: AC FMR. Data curation: AC FMR AG. Formal analysis: AC FMR SN AG. Funding acquisition: FMR. Investigation: AC FMR. Methodology: AC FMR SN. Project administration: AC FMR. Resources: AC FMR MSB MP AG. Software: AC FMR SN AG MSB. Supervision: FMR DK. Validation: AC FMR. Visualization: AC. Writing – original draft: AC FMR SN DK. Writing – review & editing: AC FMR SN DK. References 1. Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults. Med Sci Sports Exerc. 2009; 41(7):1384–91. doi: 10.1249/MSS.0b013e318199885c PMID: 19516163 2. Shephard RJ, Tudor-Locke C. The objective monitoring of physical activity: Contributions of accelero- metry to epidemiology, exercise science and rehabilitation. Cham: Springer; 2016. 3. Tudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, et al. How many steps/ day are enough? For adults. Int J Behav Nutr Phys Act. 2011; 8:79. doi: 10.1186/1479-5868-8-79 PMID: 21798015 4. Hardman AE, Stensel DJ. Physical activity and health: The evidence explained. 2nd ed. London: Rout- ledge Taylor and Francis Group; 2009. 5. Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004; 34(1):1–8. PMID: 14715035 6. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007; 298(19):2296–304. doi: 10.1001/jama.298.19.2296 PMID: 18029834 7. Tudor-Locke C, Ham SA. Walking behaviors reported in the American Time Use Survey 2003–2005. JAMA. 2008; 5(5):633–47. 8. Calabr MA, Lee JM, Saint-Maurice PF, Yoo H, Welk GJ. Validity of physical activity monitors for assess- ing lower intensity activity in adults. Int J Behav Nutr Phys Act. 2014; 11(1):1–9. 9. Aadland E, Ylvisåker E. Reliability of the Actigraph GT3X+ accelerometer in adults under free-living conditions. PloS One. 2015; 10(8):e0134606. doi: 10.1371/journal.pone.0134606 PMID: 26274586 10. Freedson P, Bowles HR, Troiano R, Haskell W. Assessment of physical activity using wearable moni- tors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc. 2012; 44(1 Suppl 1):S1–4. Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 11 / 13 11. Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. 24 Hours of sleep, seden- tary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016; 48(3):457– 65. doi: 10.1249/MSS.0000000000000778 PMID: 26484953 12. Lee JA, Williams SM, Brown DD, Laurson KR. Concurrent validation of the Actigraph GT3X+, Polar Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab- based and free-living settings. J Sports Sci. 2015; 33(10):991–1000. doi: 10.1080/02640414.2014. 981848 PMID: 25517396 13. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008; 40(1):181–8. doi: 10.1249/mss. 0b013e31815a51b3 PMID: 18091006 14. Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiologic studies: issues and challenges. Br J Sports Med. 2014; 48(3):197–201. doi: 10.1136/bjsports-2013- 093154 PMID: 24297837 15. Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res. 2012; 14(6):e161. doi: 10.2196/jmir.2171 PMID: 23171838 16. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015; 313(6):625–6. doi: 10.1001/jama.2014.17841 PMID: 25668268 17. Ranck J. The wearable computing market: a global analysis. Gigaom Pro. 2012. 18. Smart wearables—Statista Dossier. Fitbit—number of active users 2012–2015. 2015. Available: http:// www.statista.com/statistics/472600/fitbit-active-users/. 19. Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br J Sports Med. 2014; 48(13):1019–23. doi: 10.1136/bjsports-2014-093546 PMID: 24782483 20. Storm FA, Heller BW, Mazza C. Step detection and activity recognition accuracy of seven physical activity monitors. PloS One. 2015; 10(3):e0118723. doi: 10.1371/journal.pone.0118723 PMID: 25789630 21. Diaz KM, Krupka DJ, Chang MJ, Peacock J, Ma Y, Goldsmith J, et al. Fitbit(R): An accurate and reliable device for wireless physical activity tracking. Int J Cardiol. 2015; 185:138–40. doi: 10.1016/j.ijcard.2015. 03.038 PMID: 25795203 22. Nelson MB, Kaminsky LA, Dickin DC, Montoye AH. Validity of consumer-based physical activity moni- tors for specific activity types. Med Sci Sports Exerc. 2016. 23. Schneider M, Chau L. Validation of the Fitbit Zip for monitoring physical activity among free-living ado- lescents. BMC Res Notes. 2016; 9(1):448. doi: 10.1186/s13104-016-2253-6 PMID: 27655477 24. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wear- able activity trackers. Int J Behav Nutr Phys Act. 2015; 12(1):159. 25. Dierker K, Smith B. Comparison between four personal activity monitors and the Actigraph GT3X+ to measure daily steps. Med Sci Sports Exerc. 2014; 46(5):792. 26. Dominick G, Winfree K, Pohlig R, Papas M. Physical activity assessment between consumer-and research-grade accelerometers: a comparative study in free-living conditions. JMIR MHealth UHealth. 2016; 4(3):e110. doi: 10.2196/mhealth.6281 PMID: 27644334 27. Sushames A, Edwards A, Thompson F, McDermott R, Gebel K. Validity and reliability of fitbit flex for step count, moderate to vigorous physical activity and activity energy expenditure. PloS One. 2016; 11 (9):e0161224. doi: 10.1371/journal.pone.0161224 PMID: 27589592 28. Dontje ML, de Groot M, Lengton RR, van der Schans CP, Krijnen WP. Measuring steps with the Fitbit activity tracker: an inter-device reliability study. J Med Eng Technol. 2015; 39(5):286–90. doi: 10.3109/ 03091902.2015.1050125 PMID: 26017748 29. Abel T, Graf N, Niemann S. Gender bias in the assessment of physical activity in population studies. Soz Praventivmed. 2001; 46(4):268–72. PMID: 11582854 30. Marquez DX, McAuley E. Gender and acculturation influences on physical activity in Latino adults. Ann Behav Med. 2006; 31(2):138–44. doi: 10.1207/s15324796abm3102_5 PMID: 16542128 31. Australian Bureau of Statistics. Women’s participation in sport and physical activity. Canberra, ACT; 2006. 32. Chu AHY, Ng SH, Koh D, Mu¨ller-Riemenschneider F. Reliability and validity of the self-and interviewer- administered versions of the Global Physical Activity Questionnaire (GPAQ). PloS One. 2015; 10(9): e0136944. doi: 10.1371/journal.pone.0136944 PMID: 26327457 Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 12 / 13 33. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classi- fication algorithm. Med Sci Sports Exerc. 2011; 43(2):357–64. doi: 10.1249/MSS.0b013e3181ed61a3 PMID: 20581716 34. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011; 14(5):411–6. doi: 10.1016/j.jsams.2011.04.003 PMID: 21616714 35. Tudor-Locke C, Barreira TV, Schuna JMJ. Comparison of step outputs for waist and wrist accelerome- ter attachment sites. Med Sci Sports Exerc. 2015; 47(4):839–42. doi: 10.1249/MSS. 0000000000000476 PMID: 25121517 36. Fleiss JL, Levin B, Paik MC. The measurement of interrater agreement—Statistical methods for rates and proportions. 1981; 2:212–236. 37. Tudor-Locke C. Steps to better cardiovascular health: How many steps does it take to achieve good health and how confident are we in this number? Curr Cardiovasc Risk Rep. 2010; 4(4):271–6. doi: 10. 1007/s12170-010-0109-5 PMID: 20672110 38. Barriera TV, Tudor-Locke C, Champagne CM, Broyles ST, Johnson WD, Katzmarzyk PT. Comparison of GT3X accelerometer and YAMAX pedometer steps/day in a free-living sample of overweight and obese adults. J Phys Act Health. 2013; 10(2):263–70. PMID: 22821951 39. Kooiman TJ, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil. 2015; 7:24. doi: 10.1186/ s13102-015-0018-5 PMID: 26464801 40. Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013; 45(5):964. doi: 10. 1249/MSS.0b013e31827f0d9c PMID: 23247702 41. Hildebrand M, Van Hees VT, Hansen BH, Ekelund U. Age-group comparability of raw accelerometer output from wrist-and hip-worn monitors. Med Sci Sports Exerc. 2014; 46(9):1816–24. doi: 10.1249/ MSS.0000000000000289 PMID: 24887173 42. Tudor-Locke C, Barreira T, Schuna J, Mire E, Chaput J-P, Fogelholm M, et al. Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act. 2015; 12(1):11. 43. Welk G. Physical activity assessments for health-related research. Champaign, IL: Human Kinetics; 2002. 44. John D, Freedson P. Actigraph and actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc. 2012; 44(1 Suppl 1):S86–S9. Comparison of Fitbit Flex and ActiGraph for steps in free-living adults PLOS ONE | DOI:10.1371/journal.pone.0172535 February 24, 2017 13 / 13 (...TRUNCATED)

Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults.(...TRUNCATED)

02-24-2017

Chu, Anne H Y,Ng, Sheryl H X,Paknezhad, Mahsa,Gauterin, Alvaro,Koh, David,Brown, Michael S,Müller-Riemenschneider, Falk(...TRUNCATED)

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PMC10721660

Vol.:(0123456789) Sports Medicine (2023) 53 (Suppl 1):S7–S14 https://doi.org/10.1007/s40279-023-01876-3 REVIEW ARTICLE Carbohydrate Nutrition and Skill Performance in Soccer Ian Rollo1,2  · Clyde Williams2 Accepted: 8 June 2023 / Published online: 8 July 2023 © The Author(s) 2023 Abstract In soccer, players must perform a variety of sport-specific skills usually during or immediately after running, often at sprint speed. The quality of the skill performed is likely influenced by the volume of work done in attacking and defending over the duration of the match. Even the most highly skilful players succumb to the impact of fatigue both physical and mental, which may result in underperforming skills at key moments in a match. Fitness is the platform on which skill is performed during team sport. With the onset of fatigue, tired players find it ever more difficult to successfully perform basic skills. Therefore, it is not surprising that teams spend a large proportion of their training time on fitness. While acknowledging the central role of fitness in team sport, the importance of team tactics, underpinned by spatial awareness, must not be neglected. It is well established that a high-carbohydrate diet before a match and, as a supplement during match play, helps delay the onset of fatigue. There is some evidence that players ingesting carbohydrate can maintain sport-relevant skills for the duration of exercise more successfully compared with when ingesting placebo or water. However, most of the assessments of sport- specific skills have been performed in a controlled, non-contested environment. Although these methods may be judged as not ecologically valid, they do rule out the confounding influences of competition on skill performance. The aim of this brief review is to explore whether carbohydrate ingestion, while delaying fatigue during match play, may also help retain sport soccer-specific skill performance. Key Points The successful execution of repeated skilled actions is a fundamental requirement for soccer performance. Soccer players experience, to different degrees, physical and mental fatigue that have a negative impact on the performance of specific skills. Increasing muscle and liver glycogen stores before and ingesting carbohydrate during competition delays the onset of fatigue and is conducive to maintaining the execution of soccer-specific skills. Ingesting carbohydrate, at key times during competition, could counter negative feelings and improve concentra- tion, helping players maintain skill execution over the duration of exercise. 1 Introduction In soccer, players must perform a variety of sport-specific skills usually during or immediately after running at vari- ous speeds. There is an obvious link between sport-spe- cific fitness and the players’ ability to execute the relevant skill as and when it is appropriate, when defending and attacking. In all sport, skill is used as an umbrella term that includes not only physical performance of a particu- lar skill but also the complex interaction of cognitive and technical abilities to respond to the multitude of scenarios that occur in every match. While technical skills can be taught to the point of being instinctive, the cognitive skill of being able to ‘read the game’ is one that is developed over the sporting lifespan of successful players. Both the skill proficiency of the player and the number of specific technical actions reduce as a match progresses [1, 2]. In addition, the higher the tempo of a match, the sooner players begin to experience both physical (run, sprint, jump) and mental (concentration, decision-making) * Ian Rollo ian.rollo@pepsico.com 1 Gatorade Sports Science Institute, PepsiCo Life Sciences, Global R&D, Leicestershire, UK 2 School of Sports Exercise and Health Sciences, Loughborough University, Loughborough, UK S8 I. Rollo, C. Williams effects of fatigue, which often results in a decrease in skill performance [3, 4]. This is often to the frustration of coaches as well as spectators, who, for example, observe a misplaced shot, an ill-timed pass or a poor decision just when the team need it least. Therefore, teams dedicate a large proportion of their training time to fitness [5, 6]. Fatigue during prolonged exercise is closely associated with the depletion of the carbohydrate store (glycogen) in skeletal muscles (for full review see Ref. [7]). In a recent study of fatigue in a football match, Mohr et al. reported critically low glycogen levels in the skeletal muscles after 90 min of play and a further significant reduction following 30 min of extra time. Players ran less and per- formed standard skills with less accuracy than earlier in the game [8]. An early reduction in muscle and liver gly- cogen stores, during prolonged exercise, can be prevented by carbohydrate ingestion before and during exercise. Using this nutritional strategy, fatigue is delayed and per- formance sustained for longer than in the absence of this intervention [9]. In addition, several previous reviews have concluded carbohydrate ingestion also facilitates the pres- ervation of skill performance when players are fatigued [10–12]. The aim of this paper is to discuss the most recent studies investigating the effects of carbohydrate inges- tion on soccer-specific skills, and the possible role that carbohydrate ingestion plays in negating the impact that more recently reported mental fatigue has on skill perfor- mance. To inform this review article an electronic litera- ture search was undertaken using three online databases (PubMed, Web of Science, SPORTDiscus). Searches were performed using keywords from existing relevant papers. Search terms were ‘Soccer’, ‘Football’, ‘Carbohydrate’, ‘Skill’ and ‘Performance’ phrased as appropriate. Refer- ence lists of all studies and relevant systematic reviews were examined manually to identify relevant studies for this review. 2 Skill Assessment Skilled movements are physically complex but even more so when performed during match play because they involve an interaction between the physical and cognitive qualities nec- essary to achieve successful outcomes [13]. The acquisition of skills and their retention is a process that begins early in the career of soccer players. By the time they become pro- fessional players they will have achieved superior levels of soccer-specific skills, both technical and cognitive. Further- more, hours of team training and competitions help players consolidate and extend the tactical execution of their skills. Therefore, it is not surprising that the defining characteristics of professional players are their levels of sport-specific skills in addition to their superior physical attributes [14–16]. Traditionally, a team’s and players’ level of soccer-spe- cific skills have been assessed by the ‘experienced eye’ of coaches who know what is expected of professional soccer players. The technical components of skill fall into two large categories: closed (free kick, corners, penalties, throw-in) and open (passing, tackling, heading, goal shooting) skills [17]. In the modern game, skill performance is typically cap- tured via team metrics from competitive matches, for exam- ple, pass completion, interceptions, shots on target, chal- lenges won and number of interceptions [18]. An important metric is ball possession during match play. Individual play- ers must work cohesively to create space, pass and control the ball repeatedly whilst being challenged by the opposi- tion. Although percentage ball possession does not guaran- tee success, those teams with greater percentage ball pos- session perform more passes, touches per possession, shots, dribbles and final-third entries in comparison with teams with low percentage ball possession [19]. On-field analyses allow comparisons of how the speed and skill of the game changes, from match to match and beyond. For example, an analysis of the Men’s World Cup finals between 1966 and 2010 reported a 35% increase in the number of passes per minute of play, which was accompanied by a 15% increase in the speed of the match [20]. Nonetheless, while the team metrics obtained by ever more sophisticated match analysis technology are hugely informative, the impact of training, rehabilitation and nutritional intervention on individual players may be better understood by assessing their skills by objective assessments. Desirable as this is, it is difficult to design objective skill tests that reproduce all that goes into the successful execution of skills in competition. As a result, some studies have used isolated tests of soccer skill, for example, ball juggling [21], wall volley [22], heading [23], shooting [13, 24], passing [24–27] and dribbling [28]. Some laboratory-based studies provide controlled envi- ronments to investigate isolated skills and also attempt to simulate the physical demands of the sport. For example, the Soccer Match Simulation (SMS) protocol embeds soccer- specific skills to enhance the ecological validity of a previ- ously validated simulated assessment of the energy demands of a soccer match [29, 30]. However, while objective tests of skill have many advantages, they are not without several limitations. Rodriguez et al. discuss the importance of play- ing surface on the ecological validity of soccer skills tests [27, 28]. For example, dribbling a ball at speed on a smooth floor is likely a greater challenge than executing this skill on grass. Correspondingly, the footwear worn for differ- ent surfaces may not be optimal for the skill under assess- ment, such as boots versus trainers when testing shooting skill. Furthermore, the use of sport-specific materials that S9 Carbohydrate Nutrition and Skill Performance in Soccer are familiar to players, such as soccer mannequins instead of target boxes, should also be utilised [31]. Ali [17] has described the strengths and limitations of tests of soccer skill performance. 3 Carbohydrate Ingestion and Skill Fitness and skill go ‘hand-in-glove’; as players tire, they are less able to perform the relevant skills when needed [1, 2]. As mentioned earlier, there is a close association between the development of fatigue during a match and the depletion of players’ muscle glycogen stores, which becomes criti- cal should the match go into extra time, extending play to 120 min [8]. Nutritional strategies to increase the body’s glycogen stores by providing carbohydrate before and during exercise improves endurance by delaying the depletion of this essential fuel. The effectiveness of carbohydrate inges- tion applies not only to constant pace running and cycling but also to intermittent high-speed running [9], which is the common activity pattern in team sport, especially in soccer. How much carbohydrate should be consumed, and when, are questions that have led to tried and tested recommendations [5, 28, 32–37] (Table 1). While adopting nutritional strategies to delay a rapid loss of the body’s glycogen stores helps players maintain their work rate during matches, the question is whether it also helps prevent a loss of skill? A simple answer would be that if players tire less readily, after implementing a carbo- hydrate feeding strategy, then they would be better able to execute the necessary skills in match play. Unfortunately, there are too few studies to provide a definitive answer to this question. However, one study reported that when male professional soccer players ingested either a 7% carbo- hydrate–electrolyte or placebo beverage before (5 ml per kilogram body mass) and every 15 min (2 ml per kilogram body mass) during a 90 min on-field soccer match and then completed the assessment of four skills, dribbling speed, coordination, precision and power, there was a significantly improved retention of dribbling speed and precision follow- ing carbohydrate ingestion [38]. In an innovative study on the impact of carbohydrate ingestion on skill, tests were undertaken on players’ domi- nant and non-dominant limbs. Using a soccer-specific pro- tocol, higher passing scores were achieved by both dominant and non-dominant feet following the ingestion of carbohy- drate (30 g, before and at half time, compared with placebo whilst drinking water ad libitum) [27]. This effect was evi- dent from 60 min onwards. Importantly, improved perfor- mance was attained without loss of passing speed, which was better maintained in the non-dominant foot with carbo- hydrate ingestion. This observation is of interest because it is consistent with other studies in sports such as tennis, where Table 1 Carbohydrate intake recommendations for team sport Team sport exercise scenario Objectives Desired adaptation/outcome Suggested daily carbohydrate inges- tion range Considerations In-season training (1 game per week) To delay physical and mental fatigue To maintain physical qualities (and improve where possible/appropriate) To keep players injury and illness free To maintain aerobic and anaerobic fitness To at least maintain strength, power, speed To maintain lean body mass To support physical and technical perfor- mance 4–8 g/kg body mass Range accommodates variations in loads across the micro-cycle (e.g. low load days and match day − 1 carbohydrate loading protocols) as well as individual training goals (e.g. manipulation of body composi- tion to accommodate weight loss and fat loss or weight gain and lean mass gain). Practice competition carbohydrate ingestion regime Match day − 1, match day and match day + 1 6–8 g/kg body mass to elevate muscle glycogen stores Ingest 1–3 g of carbohydrate per kilogram body mass 3–4 h before a match to replenish liver glycogen stores Ingest 30 g of carbohydrate following the warm-up and during the half-time interval Ingest 1 g carbohydrate per kilogram body mass per hour with fluids after a match to start restoration of glycogen and rehydration S10 I. Rollo, C. Williams non-dominant or weaker side (backhand) shots respond posi- tively to carbohydrate ingestion, especially when fatigued [39]. The assessment of complex skilled actions on the non- dominant side may require greater activation of the central nervous system (CNS) and therefore be more susceptible to fatigue [27]. Furthermore non-dominant skilled actions may be more likely influenced by the arousal level of the player [40]. Thus, the performance of players’ non-dominant sides appears to have a greater sensitivity to carbohydrate inges- tion [27], even though the ‘non-dominant’ side is likely to be inferior in performing skills. 4 Carbohydrate Ingestion and Mental Fatigue The physiology of fatigue has been extensively studied [41]. A recent model of motor or cognitive task induced fatigue proposes that no single factor is responsible for declines in skill performance. Instead, fatigue is considered a psycho- physiological condition. Motor fatigue and perceived fatigue are interdependent but hinge on various determinants and depend on modulating factors such as age, sex and specific skill characteristics [42]. Mental fatigue is defined as a psy- chobiological state that arises during prolonged demanding cognitive activity and results in an acute feeling of tired- ness and/or a decreased cognitive ability as well as mood changes [43, 44]. Mental fatigue can reduce physical capac- ity, assessed through reduced time to exhaustion and ele- vated rating of perceived exertion (RPE) [45], and has been shown to fluctuate throughout a competitive season [46]. To highlight this point, mental fatigue has been found, in one review, to have a negative influence on 37% of soccer- specific skills (n = 92) [43]. Mental fatigue has been recognised as a key considera- tion in team sport, due to the associated negative impact on physical, technical, decision-making and tactical perfor- mance [47]. Contributing factors to mental fatigue in team sport environments include but are not limited to prolonged cognitive demands, team meetings, travel and the inability to ‘switch off’ [48, 49]. Of note is the approach taken in laboratory studies which use the repeated execution of inherent sport-specific skills to induce mental fatigue [50]. Thus, tracking skill execution may also be important because it might reflect the presence of both mental and physical fatigue. Correspondingly, moni- toring mental fatigue has been recommended in team sport to provide an overall picture of how players are coping with the demands of training and competition [51]. Therefore, strategies are used to help avoid mental fatigue, for example, displacement activities, such as changes in training routines, environment and, of course, adequate rest and recovery. Increasing dietary carbohydrate while improving exercise capacity both in training and in competition may also be a mood-changing countermeasure to mental fatigue [52, 53]. If players are feeling good rather than bad (pleasure–dis- pleasure) and energized (i.e. in an activated state) before and during matches, then it is more likely that they will per- form better [40, 54]. For example, Backhouse et al. have shown that the ingestion of carbohydrate elevated perceived activation during the final 30 min of 120-min of intermit- tent running exercise [55] and also attenuated the decline in pleasure–displeasure during a 120-min bout of cycling [56]. Administering both a Feeling Scale (FS) and an RPE scale allows a measure of not only ‘what’ (RPE) but also ‘how’ (FS) a person feels [57] but is rarely administered during skill intervention studies or applied settings. A recent review identified mouth rinsing and expectorat- ing a carbohydrate beverage as a potential acute counter- measure to mental fatigue [58]. The recognition of carbo- hydrate in the mouth, when administered immediately after a mentally fatiguing task, was linked to increased excitabil- ity of corticomotor pathways [59, 60]. Furthermore, there appears to be a direct link between improvements in task- specific activity and activation within the primary senso- rimotor cortex in response to oral carbohydrate signalling [61]. These results contribute to a possible explanation for improved high-intensity intermittent running performance in response to mouth rinsing with a 10% carbohydrate bev- erage [62, 63]. Although not all studies report this effect [64], central activation mediated by the ingestion of carbo- hydrate may contribute to the better retention of sprint and technical performance observed early in exercise or in the absence of hypoglycaemia [27, 28, 65]. While mouth rins- ing with a carbohydrate beverage has been shown to benefit complex whole-body skilled actions in fencers, compared with taste-matched placebos [66], the impact on soccer skill performance is yet to be investigated. Furthermore, it is also important to note that mouth rinsing with non-sweet car- bohydrate activates the reward centres of the brain and so may contribute to the ‘feel good’ sensation that may counter mental fatigue [67]. Nevertheless, these findings should be considered as an additional benefit to carbohydrate inges- tion, during or after exercise, when substrate delivery and replenishment of glycogen stores are the respective priorities [68–70]. These responses to carbohydrate ingestion may not be sur- prising bearing in mind that glucose is the main fuel for the brain and CNS [71]. For optimum functioning of the brain and CNS, glucose homeostasis must be maintained even dur- ing a wide range of conditions. Should blood glucose fall to hypoglycaemic levels, then the neural drive to skeletal mus- cles will be compromised; however, it is restored following the ingestion of carbohydrate [72]. During exercise, the rate of glucose release from the liver into the blood increases to match the glucose uptake by contracting muscle [73]. In most S11 Carbohydrate Nutrition and Skill Performance in Soccer team sport, blood glucose concentrations are well maintained over the duration of competition (80–90 min) and extra time (120 min in soccer) in well-fed individuals [74]. Nevertheless, carbohydrate ingestion at the onset of exercise is an effec- tive strategy not only to top up muscle glycogen stores but also because it temporarily inhibits hepatic glucose release in a dose-dependent manner, and so conserves liver glycogen stores [75, 76]. Carbohydrate ingestion, as a means of pre- serving the finite store of liver glycogen, will maintain blood glucose concentrations and performance late in exercise. This strategy is particularly beneficial when matches extend to extra time [8, 77]. Of interest is the observation that elevated blood glucose concentrations are associated with improved skill performance in comparison with euglycaemia [27, 28, 65, 78]. An immediate explanation for this observation is not apparent other than that glucose is a fuel for the brain [79, 80]. However, the brain is sensitive to changes in blood glucose, and the rate of change may act to monitor the availability of whole-body carbohydrate stores. 5 Conclusion Participants in team sport experience, to different degrees, physical and mental fatigue that have a negative impact on the performance of sport-specific skills. The complex series of events between brain and skeletal muscle that interact to minimise the impact of physical and mental fatigue on the performance of skills during competition, following carbo- hydrate feeding, is summarised in Fig. 1. Nutritional strate- gies that increase muscle and liver glycogen stores prior to competition and provide carbohydrate during competition maintain work rate by delaying the onset of fatigue. This effect of carbohydrate ingestion is, in itself, conducive to maintaining the execution of sport-specific skill. Further- more, ingesting carbohydrate, at key times during competi- tion, could counter negative feelings and improve concentra- tion, thereby helping players maintain skill execution over the duration of exercise. Acknowledgements This supplement is supported by the Gatorade Sports Science Institute (GSSI). The supplement was guest edited by Lawrence L. Spriet, who convened a virtual meeting of the GSSI Expert Panel in October 2022 and received honoraria from the GSSI, a division of PepsiCo, Inc., for his participation in the meeting. Dr Spriet received no honoraria for guest editing this supplement. Dr Spriet Fig. 1 Translating thoughts into skilled actions. The electro-chemical chain of events between the brain and skeletal muscles, and how car- bohydrate ingestion may impact skill performance. BM body mass, SR sarcoplasmic reticulum, Ca2+ calcium, Na+/K+ sodium–potassium pump, ATP adenosine triphosphate. ‘+’ = positive influence upon, ‘−’ = negative influence upon. Mood, motivation, RPE [52, 55, 58], facilitation of corticomotor outputs [60, 61], blood glucose availabil- ity, hepatic glycogen preservation [75, 76, 81, 82], muscle innerva- tion: SR calcium handling [83], ATP generation [83–85] S12 I. Rollo, C. Williams suggested peer reviewers for each paper, which were sent to the Sports Medicine Editor-in-Chief for approval, prior to any reviewers being approached. Dr Spriet provided comments on each paper and made an editorial decision based on comments from the peer reviewers and the Editor-in-Chief. Where decisions were uncertain, Dr Spriet consulted with the Editor-in-Chief. The views expressed in this manuscript are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc. The authors would like to acknowledge and thank all previous and existing colleagues and collaborators. Declarations Funding This article is based on a presentation by Ian Rollo to the GSSI Expert Panel in October 2022. No honorarium for participation in or preparation of the article for that meeting was provided by the GSSI. No other sources of funding were utilized by the authors in the preparation of the article for this supplement. Conflict of interest Ian Rollo is an employee of the Gatorade Sports Science Institute. However, the views expressed in this manuscript are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc. Clyde Williams declares no conflicts of inter- est relevant to the content of this review. While this author previously presented to the GSSI Expert Panel in 2015, and funding for participa- tion in that meeting together with an honorarium were provided by the GSSI, the honorarium was donated to charity. Author contributions IR conceived the idea for this review. IR and CW conducted the literature search and selected the articles for inclusion in the review. IR and CW co-wrote the first draft and revised the original manuscript. Both authors read and approved the final version. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Harper LD, West DJ, Stevenson E, Russell M. Technical perfor- mance reduces during the extra-time period of professional soccer match-play. PLoS ONE. 2014;9(10): e110995. 2. Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisloff U. Technical performance during soccer matches of the Italian Serie A league: effect of fatigue and competitive level. J Sci Med Sport. 2009;12(1):227–33. 3. Mohr M, Krustrup P, Bangsbo J. Fatigue in soccer: a brief review. J Sports Sci. 2005;23(6):593–9. 4. Smith MR, Zeuwts L, Lenoir M, Hens N, De Jong LM, Coutts AJ. Mental fatigue impairs soccer-specific decision-making skill. J Sports Sci. 2016;34(14):1297–304. 5. Anderson L, Orme P, Di Michele R, Close GL, Morgans R, Drust B, Morton JP. Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English Premier League: implications for car- bohydrate periodisation. J Sports Sci. 2016;34(13):1250–9. 6. Papadakis L, Tymvios C, Patras K. The relationship between training load and fitness indices over a pre-season in professional soccer players. J Sports Med Phys Fit. 2020;60(3):329–37. 7. Mohr M, Vigh-Larsen JF, Krustrup P. Muscle glycogen in Elite Soccer—a perspective on the implication for performance, fatigue, and recovery. Front Sports Active Living. 2022;4: 876534. 8. Mohr M, Ermidis G, Jamustas AZ, Vigh-Larsen J, Poulios A, Draganidis D, Papanikolaou K, Tsimeas P, Batsilas D, Loules G, Batrakoulis A, Sovatzidis A, Nielsen JL, Tzatzakis T, Deli CK, Nybo L, Krustrup P, Fatouros IG. Extended match time exacerbates fatigue and impacts physiological responses in male soccer players. Med Sci Sports Exerc. 2022;55(1):80–92. 9. Williams C, Rollo I. Carbohydrate nutrition and team sport per- formance. Sports Med. 2015;45(Suppl 1):S13-22. 10. Hills SP, Russell M. Carbohydrates for soccer: a focus on skilled actions and half-time practices. Nutrients. 2017;10(1):22–32. 11. Baker LB, Rollo I, Stein KW, Jeukendrup AE. Acute effects of carbohydrate supplementation on intermittent sports perfor- mance. Nutrients. 2015;7(7):5733–63. 12. Russell M, Kingsley M. The efficacy of acute nutritional interventions on soccer skill performance. Sports Med. 2014;44(7):957–70. 13. Haaland E, Hoff J. Non-dominant leg training improves the bilateral motor performance of soccer players. Scand J Med Sci Sports. 2003;13(3):179–84. 14. Slimani M, Nikolaidis PT. Anthropometric and physiological characteristics of male soccer players according to their competi- tive level, playing position and age group: a systematic review. J Sports Med Phys Fit. 2019;59(1):141–63. 15. Cometti G, Maffiuletti NA, Pousson M, Chatard JC, Maffulli N. Isokinetic strength and anaerobic power of elite, subelite and ama- teur French soccer players. Int J Sports Med. 2001;22(1):45–51. 16. Gouveia JN, França C, Martins F, Henriques R, Nascimento MM, Ihle A, Sarmento H, Przednowek K, Martinho D, Gouveia ÉR. Characterization of static strength, vertical jumping, and isokinetic strength in soccer players according to age, competi- tive level, and field position. Int J Environ Res Public Health. 2023;20(3):1799. https:// doi. org/ 10. 3390/ ijerp h2003 1799 17. Ali A. Measuring soccer skill performance: a review. Scand J Med Sci Sports. 2011;21(2):170–83. 18. Andrzejewski M, Oliva-Lozano JM, Chmura P, Chmura J, Czarniecki S, Kowalczuk E, Rokita A, Muyor JM, Konefal M. Analysis of team success based on match technical and running performance in a professional soccer league. BMC Sports Sci Med Rehabil. 2022;14(1):82. 19. Bradley PS, Lago-Penas C, Rey E, Gomez Diaz A. The effect of high and low percentage ball possession on physical and technical profiles in English FA Premier League soccer matches. J Sports Sci. 2013;31(12):1261–70. 20. Wallace JL, Norton KI. Evolution of World Cup soccer final games 1966–2010: game structure, speed and play patterns. J Sci Med Sport. 2014;17(2):223–8. 21. Hoare DG, Warr CR. Talent identification and women’s soccer: an Australian experience. J Sports Sci. 2000;18(9):751–8. 22. Vanderford ML, Meyers MC, Skelly WA, Stewart CC, Hamilton KL. Physiological and sport-specific skill response of Olympic youth soccer athletes. J Strength Cond Res. 2004;18(2):334–42. 23. Rosch D, Hodgson R, Peterson TL, Graf-Baumann T, Junge A, Chomiak J, Dvorak J. Assessment and evaluation of football per- formance. Am J Sports Med. 2000;28(5 Suppl):S29-39. 24. Ali A, Williams C. Carbohydrate ingestion and soccer skill per- formance during prolonged intermittent exercise. J Sports Sci. 2009;27(14):1499–508. S13 Carbohydrate Nutrition and Skill Performance in Soccer 25. Rostgaard T, Iaia FM, Simonsen DS, Bangsbo J. A test to evaluate the physical impact on technical performance in soccer. J Strength Cond Res. 2008;22(1):283–92. 26. Bendiksen M, Bischoff R, Randers MB, Mohr M, Rollo I, Suetta C, Bangsbo J, Krustrup P. The Copenhagen Soccer Test: physi- ological response and fatigue development. Med Sci Sports Exerc. 2012;44(8):1595–603. 27. Rodriguez-Giustiniani P, Rollo I, Witard OC, Galloway SDR. Ingesting a 12% carbohydrate-electrolyte beverage before each half of a soccer match simulation facilitates retention of passing performance and improves high-intensity running capacity in academy players. Int J Sport Nutr Exerc Metab. 2019;29(4):397–405. 28. Harper LD, Stevenson EJ, Rollo I, Russell M. The influence of a 12% carbohydrate-electrolyte beverage on self-paced soccer- specific exercise performance. J Sci Med Sport. 2017;12:1123–9. 29. Russell M, Rees G, Benton D, Kingsley M. An exercise pro- tocol that replicates soccer match-play. Int J Sports Med. 2011;32(7):511–8. 30. Nicholas CW, Nuttall FE, Williams C. The Loughborough Inter- mittent Shuttle Test: a field test that simulates the activity pattern of soccer. J Sports Sci. 2000;18(2):97–104. 31. Rodriguez-Giustiniani P, Rollo I, Galloway SDR. A preliminary study of the reliability of soccer skill tests within a modified soc- cer match simulation protocol. Sci Med Footb. 2021;6(3):363–71. 32. Collins J, Maughan RJ, Gleeson M, Bilsborough J, Jeukendrup A, Morton JP, Phillips SM, Armstrong L, Burke LM, Close GL, Duffield R, Larson-Meyer E, Louis J, Medina D, Meyer F, Rollo I, Sundgot-Borgen J, Wall BT, Boullosa B, Dupont G, Lizarraga A, Res P, Bizzini M, Castagna C, Cowie CM, D’Hooghe M, Geyer H, Meyer T, Papadimitriou N, Vouillamoz M, McCall A. UEFA expert group statement on nutrition in elite football current evidence to inform practical recommendations and guide future research. Br J Sports Med. 2021;55(8):416. 33. Thomas DT, Erdman KA, Burke LM. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: nutrition and athletic performance. J Acad Nutr Diet. 2016;116(3):501–28. 34. Rollo I, Randell RK, Baker L, Leyes JY, Medina Leal D, Lizarraga A, Mesalles J, Jeukendrup AE, James LJ, Carter JM. Fluid bal- ance, sweat Na+ losses, and carbohydrate intake of elite male soccer players in response to low and high training intensities in cool and hot environments. Nutrients. 2021;13(2):401. https:// doi. org/ 10. 3390/ nu130 20401 35. Moss SL, Randell RK, Burgess D, Ridley S, ÓCairealláin C, Alli- son R, Rollo I. Assessment of energy availability and associated risk factors in professional female soccer players. Eur J Sport Sci. 2020;6:861–70. 36. Funnell MP, Dykes NR, Owen EJ, Mears SA, Rollo I, James LJ. Ecologically valid carbohydrate intake during soccer-specific exercise does not affect running performance in a fed state. Nutri- ents. 2017;9(1):39. https:// doi. org/ 10. 3390/ nu901 0039 37. Burke LM, Hawley JA, Wong SH, Jeukendrup AE. Carbohy- drates for training and competition. J Sports Sci. 2011;29(Suppl 1):S17-27. 38. Ostojic SM, Mazic S. Effects of a carbohydrate-electrolyte drink on specific soccer tests and performance. J Sports Sci Med. 2002;1(2):47–53. 39. McRae KA, Galloway SD. Carbohydrate-electrolyte drink inges- tion and skill performance during and after 2 hr of indoor tennis match play. Int J Sport Nutr Exerc Metab. 2012;22(1):38–46. 40. McMorris T, Graydon J. The effect of exercise on cogni- tive performance in soccer-specific tests. J Sports Sci. 1997;15(5):459–68. 41. Enoka RM, Baudry S, Rudroff T, Farina D, Klass M, Duchateau J. Unraveling the neurophysiology of muscle fatigue. J Electromyogr Kinesiol. 2011;21(2):208–19. 42. Behrens M, Gube M, Chaabene H, Prieske O, Zenon A, Broscheid KC, Schega L, Husmann F, Weippert M. Fatigue and human per- formance: an updated framework. Sports Med. 2023;53(1):7–31. https:// doi. org/ 10. 1007/ s40279- 022- 01748-2 43. Habay J, Van Cutsem J, Verschueren J, De Bock S, Proost M, De Wachter J, Tassignon B, Meeusen R, Roelands B. Mental fatigue and sport-specific psychomotor performance: a systematic review. Sports Med. 2021;51(7):1527–48. 44. Roelands B, Kelly V, Russell S, Habay J. The physiological nature of mental fatigue: current knowledge and future avenues for sport science. Int J Sports Physiol Perform. 2022;17(2):149–50. 45. Marcora SM, Staiano W, Manning V. Mental fatigue impairs physical performance in humans. J Appl Physiol (1985). 2009;106(3):857–64. 46. Russell S, Jenkins DG, Halson SL, Juliff LE, Kelly VG. How do elite female team sport athletes experience mental fatigue? Com- parison between international competition, training and prepara- tion camps. Eur J Sport Sci. 2022;22(6):877–87. 47. Smith MR, Thompson C, Marcora SM, Skorski S, Meyer T, Coutts AJ. Mental fatigue and soccer: current knowledge and future directions. Sports Med. 2018;48(7):1525–32. 48. Thompson CJ, Noon M, Towlson C, Perry J, Coutts AJ, Harper LD, Skorski S, Smith MR, Barrett S, Meyer T. Understanding the presence of mental fatigue in English academy soccer players. J Sports Sci. 2020;38(13):1524–30. 49. Thompson CJ, Smith A, Coutts AJ, Skorski S, Datson N, Smith MR, Meyer T. Understanding the presence of mental fatigue in elite female football. Res Q Exerc Sport. 2022;93(3):504–15. 50. Bian C, Ali A, Nassis GP, Li Y. Repeated interval Loughborough soccer passing tests: an ecologically valid motor task to induce mental fatigue in soccer. Front Physiol. 2021;12: 803528. 51. Thompson CJ, Fransen J, Skorski S, Smith MR, Meyer T, Bar- rett S, Coutts AJ. Mental fatigue in football: is it time to shift the goalposts? An evaluation of the current methodology. Sports Med. 2019;49(2):177–83. 52. Achten J, Halson SL, Moseley L, Rayson MP, Casey A, Jeuken- drup AE. Higher dietary carbohydrate content during intensified running training results in better maintenance of performance and mood state. J Appl Physiol. 2004;96(4):1331–40. 53. Killer SC, Svendsen IS, Jeukendrup AE, Gleeson M. Evidence of disturbed sleep and mood state in well-trained athletes during short-term intensified training with and without a high carbohy- drate nutritional intervention. J Sports Sci. 2017;35(14):1402–10. 54. Acevedo E, Gill D, Goldfarb A, Boyer B. Affect and per- ceived exertion during a two-hour run. Int J Sport Psychol. 1996;27:286–92. 55. Backhouse SH, Ali A, Biddle SJ, Williams C. Carbohydrate ingestion during prolonged high-intensity intermittent exercise: impact on affect and perceived exertion. Scand J Med Sci Sports. 2007;17(5):605–10. 56. Backhouse SH, Bishop NC, Biddle SJ, Williams C. Effect of car- bohydrate and prolonged exercise on affect and perceived exer- tion. Med Sci Sports Exerc. 2005;37(10):1768–73. 57. Hardy CJ, Rejeski W. Not what, but how ones feels: the meas- urement of affect during exercise. J Sport Exerc Psychol. 1989;11:304–17. 58. Proost M, Habay J, De Wachter J, De Pauw K, Rattray B, Meeusen R, Roelands B, Van Cutsem J. How to tackle mental fatigue: a sys- tematic review of potential countermeasures and their underlying mechanisms. Sports Med. 2022;52(9):2129–58. 59. Bailey SP, Harris GK, Lewis K, Llewellyn TA, Watkins R, Weaver MA, Roelands B, Van Cutsem J, Folger SF. Impact of a carbohy- drate mouth rinse on corticomotor excitability after mental fatigue S14 I. Rollo, C. Williams in healthy college-aged subjects. Brain Sci. 2021;11(8):972. https:// doi. org/ 10. 3390/ brain sci11 080972 60. Gant N, Stinear CM, Byblow WD. Carbohydrate in the mouth immediately facilitates motor output. Brain Res. 2010;1350:151–8. 61. Turner CE, Byblow WD, Stinear CM, Gant N. Carbohydrate in the mouth enhances activation of brain circuitry involved in motor performance and sensory perception. Appetite. 2014;80:212–9. 62. Rollo I, Homewood G, Williams C, Carter J, Goosey-Tolfrey VL. The influence of carbohydrate mouth rinse on self-selected intermittent running performance. Int J Sport Nutr Exerc Metab. 2015;25(6):550–8. 63. Kasper AM, Cocking S, Cockayne M, Barnard M, Tench J, Parker L, McAndrew J, Langan-Evans C, Close GL, Morton JP. Carbo- hydrate mouth rinse and caffeine improves high-intensity interval running capacity when carbohydrate restricted. Eur J Sport Sci. 2016;16(5):560–8. 64. Gough LA, Faghy M, Clarke N, Kelly AL, Cole M, Lun Foo W. No independent or synergistic effects of carbohydrate-caffeine mouth rinse on repeated sprint performance during simulated soccer match play in male recreational soccer players. Sci Med Footb. 2022;6(4):519–27. 65. Ali A, Williams C, Nicholas CW, Foskett A. The influence of carbohydrate-electrolyte ingestion on soccer skill performance. Med Sci Sports Exerc. 2007;39(11):1969–76. 66. Rowlatt G, Bottoms L, Edmonds CJ, Buscombe R. The effect of carbohydrate mouth rinsing on fencing performance and cogni- tive function following fatigue-inducing fencing. Eur J Sport Sci. 2017;17(4):433–40. 67. Chambers ES, Bridge MW, Jones DA. Carbohydrate sensing in the human mouth: effects on exercise performance and brain activity. J Physiol. 2009;587(Pt 8):1779–94. 68. Rollo I, Gonzalez JT, Fuchs CJ, van Loon LJC, Williams C. Pri- mary, secondary, and tertiary effects of carbohydrate ingestion during exercise. Sports Med. 2020;50(11):1863–71. 69. Erith S, Williams C, Stevenson E, Chamberlain S, Crews P, Rushbury I. The effect of high carbohydrate meals with different glycemic indices on recovery of performance during prolonged intermittent high-intensity shuttle running. Int J Sport Nutr Exerc Metab. 2006;16(4):393–404. 70. Rollo I, Williams C, Nevill M. Influence of ingesting versus mouth rinsing a carbohydrate solution during a 1-h run. Med Sci Sports Exerc. 2011;43(3):468–75. 71. Mergenthaler P, Lindauer U, Dienel GA, Meisel A. Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends Neurosci. 2013;36(10):587–97. 72. Nybo L. CNS fatigue and prolonged exercise: effect of glucose supplementation. Med Sci Sports Exerc. 2003;35(4):589–94. 73. Wasserman DH. Four grams of glucose. Am J Physiol-Endocrinol Metab. 2009;296(1):E11-21. 74. Harper LD, Briggs MA, McNamee G, West DJ, Kilduff LP, Stevenson E, Russell M. Physiological and performance effects of carbohydrate gels consumed prior to the extra-time period of prolonged simulated soccer match-play. J Sci Med Sport. 2016;19(6):509–14. 75. Jeukendrup AE, Wagenmakers AJ, Stegen JH, Gijsen AP, Brouns F, Saris WH. Carbohydrate ingestion can completely suppress endogenous glucose production during exercise. Am J Physiol. 1999;276(4 Pt 1):E672–83. 76. Newell ML, Wallis GA, Hunter AM, Tipton KD, Galloway SDR. Metabolic responses to carbohydrate ingestion during exercise: associations between carbohydrate dose and endurance perfor- mance. Nutrients. 2018;10(1):37. https:// doi. org/ 10. 3390/ nu100 10037 77. Field A, Naughton RJ, Haines M, Lui S, Corr LD, Russell M, Page RM, Harper LD. The demands of the extra-time period of soccer: a systematic review. J Sport Health Sci. 2022;11(3):403–14. 78. Ali A, Williams C. Carbohydrate ingestion and soccer skill per- formance during prolonged intermittent exercise. J Sports Sci. 2009;27(14):1499–508. https:// doi. org/ 10. 1080/ 02640 41090 33347 72 79. Lopez-Gambero AJ, Martinez F, Salazar K, Cifuentes M, Nualart F. Brain glucose-sensing mechanism and energy homeostasis. Mol Neurobiol. 2019;56(2):769–96. 80. van Praag H, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, glucose homeostasis, and the brain. J Neurosci. 2014;34(46):15139–49. 81. Gonzalez JT, Fuchs CJ, Betts JA, van Loon LJ. Liver glycogen metabolism during and after prolonged endurance-type exercise. Am J Physiol-Endocrinol Metab. 2016;311(3):E543–53. 82. Fuchs CJ, Gonzalez JT, Beelen M, Cermak NM, Smith FE, Thelwall PE, Taylor R, Trenell MI, Stevenson EJ, van Loon LJ. Sucrose ingestion after exhaustive exercise accelerates liver, but not muscle glycogen repletion compared with glucose ingestion in trained athletes. J Appl Physiol (1985). 2016;120(11):1328–34. 83. Ortenblad N, Nielsen J, Saltin B, Holmberg HC. Role of glyco- gen availability in sarcoplasmic reticulum Ca2+ kinetics in human skeletal muscle. J Physiol. 2011;589(Pt 3):711–25. 84. Duhamel TA, Stewart RD, Tupling AR, Ouyang J, Green HJ. Mus- cle sarcoplasmic reticulum calcium regulation in humans during consecutive days of exercise and recovery. J Appl Physiol (1985). 2007;103(4):1212–20. 85. Nielsen J, Holmberg HC, Schroder HD, Saltin B, Ortenblad N. Human skeletal muscle glycogen utilization in exhaustive exer- cise: role of subcellular localization and fibre type. J Physiol. 2011;589(Pt 11):2871–85. (...TRUNCATED)

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RESEARCH ARTICLE Effects of a period without mandatory physical training on maximum oxygen uptake and anthropometric parameters in naval cadets A´ lvaro Huerta OjedaID*☯, Guillermo Barahona-FuentesID☯, Sergio Galdames Maliqueo☯ Grupo de Investigacio´n en Salud, Actividad Fı´sica y Deporte ISAFYD, Escuela de Educacio´n Fı´sica, Universidad de Las Ame´ricas, sede Viña del Mar, Chile ☯ These authors contributed equally to this work. * achuertao@yahoo.es Abstract The effects of a period without physical training on the civilian population are well estab- lished. However, no studies show the effects of a period without mandatory physical training on maximum oxygen uptake (VO2 max) and anthropometric parameters in naval cadets. This study aimed to investigate changes in VO2 max and anthropometric parameters after 12 weeks without mandatory physical training in naval cadets. The sample was 38 healthy and physically active naval cadets. The measured variables, including VO2 max and anthro- pometric parameters, were evaluated through the 12-minute race test (12MRT) and the somatotype. Both variables had a separation of 12 weeks without mandatory physical train- ing. A t-test for related samples was used to evidence changes between the test and post- test; effect size was calculated through Cohen’s d-test. Distance in 12MRT and VO2 max showed significant decreases at the end of 12 weeks without mandatory physical training (p < 0.001). Likewise, the tricipital skinfold thickness and the endomorphic component showed significant increases (p < 0.05). 12 weeks without mandatory physical training significantly reduces the VO2 max in naval cadets. Simultaneously, the same period without physical training increases both the tricipital skinfold thickness and the endomorphic component in this population. Introduction Increased physical capabilities through strength training [1, 2] and aerobic capacity [3] have been associated with health, quality of life, and sports performance benefits [1–3]. In this sense, people included in strength training have shown neuronal and morphological adapta- tions [4]; these two adaptations, generated by strength training, allow for the improvement of both the metabolic health [5] and the quality of life of people [6]. At the same time, aerobic training has reported significant decreases in cardiovascular risk factors [7], as well as an increase in maximum oxygen uptake (VO2 max) [3]. Specifically, the VO2 max has a direct PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Huerta Ojeda A´, Barahona-Fuentes G, Galdames Maliqueo S (2021) Effects of a period without mandatory physical training on maximum oxygen uptake and anthropometric parameters in naval cadets. PLoS ONE 16(6): e0251516. https:// doi.org/10.1371/journal.pone.0251516 Editor: Randy Wayne Bryner, West Virginia University, UNITED STATES Received: October 10, 2020 Accepted: April 27, 2021 Published: June 2, 2021 Copyright: © 2021 Huerta Ojeda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying this study are publicly available at: https://doi.org/ 10.6084/m9.figshare.14049590. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. association with the quality of life of people [8]. These morphological and metabolic changes, triggered by force training or aerobic training, are experienced by both the civilian population [9] and the military and naval population [10–12]; in the latter, they provide specific physical characteristics that allow missions to be carried out efficiently and with a low risk of injury [13]. Scientific evidence shows that physical training acts as a physiological stressor, increasing energy expenditure [14], anabolic hormone concentrations [15], arterial diameter, and blood flow [16]. These responses to physical training contribute to a physiological adaptation of the body [17], specifical adaptations of muscles [18], and bone tissues [19]. In this sense, a recently published meta-analysis showed the benefits of eccentric strength training through isoinertial devices; the study results showed increases in strength, power, and muscle size with this train- ing [20]. Concerning aerobic training, these stimuli have been considered as the primary method to improve markers of cardiorespiratory fitness, mainly VO2 max [21]. Additionally, physical training carried out regularly, and with the principles of intensity, volume, and fre- quency, will minimize muscular fatigue [22] and favor the physiological adaptations of the body [17]. Despite the above, there is also a transition phase in sports periodization [23]; this stage corresponds to the interruption of physical training [24], which can be short term (less than four weeks) or long term (more than four weeks) [25]. However, if professionals do not control the transition phase, there is a high probability of provoking a detraining [25]. In this way, a period without physical training can generate a partial or total loss of morphological adaptations, physiological adaptations, and physical performance [26], as well as cause alter- ations in the psychological well-being of the population [27]. The sports transition phase is an opportunity for the physical recovery of athletes [23]. However, there are unplanned situations that generate periods of non-physical training in the population [28–30], for example, the period of vacation experienced by students each year [28] or the current period of confinement generated by COVID-19 [30]. Regardless of the reasons, an extended time-period without physical training has been shown to negatively influence ath- letes’ body composition [23], increasing fat mass and decreasing lean mass [31–33]. It has also been shown that a period without physical training of fewer than eight weeks leads to a decrease in muscle cross-section [34], decreases in maximum strength [35], and a reduction in VO2 max in both the civilian [36] and naval [37] populations. Currently, naval personnel has been the subject of several research studies [38, 39]. One of the reasons for the growing number of investigations in this sample is that the Chilean Navy comprises more than 25,000 personnel. Of this number, 9.6% (equivalent to 2,400 personnel) corresponds to naval officers, all trained at the Arturo Prat Naval Academy [40]. These figures show several aspects, such as the high number of officers [40] and, therefore, the need for this population to be studied from a psychological [11, 13], health [12] and physical [10, 38] perfor- mance perspective. This last dimension includes the transition phase considering that we hypothesize that naval cadets decrease their physical condition, associated with VO2 max and anthropometric parameters, after a period without mandatory physical training; thus, with correctly applied training loads, physical fitness loss in this phase could be avoided [23–25]. Despite the existence of studies showing a decrease in the physical condition and anthropo- metric parameters after a period without physical training in some segments of the population [23, 31–33], the available evidence in the naval population is scarce and limited [37]. Likewise, and as far as knowledge goes, no studies evidence the effects of periods without physical train- ing on VO2 max and anthropometric parameters in naval cadets from 18 to 25 years old. Con- sequently, this study aimed to evidence the changes in VO2 max and anthropometric parameters after 12 weeks without mandatory physical training in naval cadets from 18 to 25 years old. PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 2 / 15 Materials and methods Research design This study was empirical research with a manipulative, quasi-experimental strategy with a lon- gitudinal design with repeated means [41]. To highlight the changes in VO2 max and anthro- pometric parameters, the 12-minute race test (12MRT) and the somatotype were evaluated 12 weeks apart, a period without mandatory physical training (Fig 1). Procedures As a first action, all participants who voluntarily accepted to be part of the study (non-probabi- listic sample) were recruited. The purpose and procedures of the study were indicated in an informative talk. The inclusion criteria were that all participants should be healthy, physically active [21] and between 18 and 25 years of age, while the exclusion criteria were: prevalence of musculoskeletal injuries, pre-existing cardiac pathologies, abnormal respiratory and cardiac responses during the familiarization period and inability to perform the 12MRT. All partici- pants were asked not to engage in physical training that would generate nervous or musculo- skeletal fatigue 48 hours before the measurements and refrain from ingesting caffeine or any substance that could increase their metabolism during the assessment. Finally, only those par- ticipants who signed informed consent were subjected to 12MRT and somatotype evaluations. Participants Thirty-eight healthy and physically active naval cadets volunteered to participate in this study (Table 1). The type of sampling was non-probabilistic for convenience. All participants were informed of the study objective and possible risks of the experiment. Indeed, all participants signed the informed consent form before the implementation of the protocols. The informed consent and the study were approved by the Human Research Committee of the University of Las Americas (registry number CEC-FP-2020011). The informed consent and the study were conducted under the Declaration of Helsinki (WMA 2000, Bosˇnjak 2001, Tyebkhan 2003), which sets out the fundamental ethical principles for research with human subjects. Fig 1. Research design. 12MRT: 12-minute race test. https://doi.org/10.1371/journal.pone.0251516.g001 Table 1. Characterization of the participants. Women (n = 8) Men (n = 30) All (n = 38) mean ± SD (min–max) Mean ± SD (min–max) mean ± SD (min–max) Age (years) 21.0 ± 1.51 (19–23) 20.5 ± 1.22 (18–24) 20.6 ± 1.28 (18–24) BMI (kg/m2) 21.9 ± 1.79 (20.2–25.5) 22.7 ± 1.69 (20.4–26.7) 22.5 ± 1.72 (20.2–26.7) % Fat 23.3 ± 4.7 (18.5–33.1) 12.6 ± 2.2 (9.3–18.1) 14.9 ± 5.2 (9.3–33.1) VO2 max (mLO2
kg–1
min–1) 46.7 ± 3.9 (42.6–51.5) 59.3 ± 4.7 (50.9–65.5) 56.6 ± 6.9 (42.6–65.5) SD: standard deviation; kg/m2: kilograms per square meters; min: minimum; max: maximum; %: percentage; VO2 max: maximum oxygen uptake; mLO2
kg–1
min–1: milliliters of oxygen per kilogram of body mass per minute. https://doi.org/10.1371/journal.pone.0251516.t001 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 3 / 15 Somatotype evaluation The somatotype corresponds to the shape of the human body. It is obtained by analyzing the arm and leg’s circumferences, the humerus and femur’s diameters, four skinfolds (tricipital, subscapular, supra-iliac, and mid-calf), and the weight and height of a person. Body shape can be represented two-dimensionally through the somatochart or three-dimensionally through the compogram; the latter representation corresponds to three numerical values representing the endomorphic, mesomorphic, and ectomorphic components of a participant (always in that order) [42]. To represent a participant’s morphology, Berral [42] recommends using both the somatochart and the compogram since using only the somatochart can generate an error in interpreting the results; for example, values 3–5–3 and 4–6–4 would be represented with the same point on the somatochart [42]. Body mass and height. The method used to determine the participants’ somatotype was pro- posed by Carter & Heath [43]. The body mass (kg) was evaluated through a Tanita Inner Scan BC-5541 digital scale, with the participants barefoot, in shorts, and wearing a light shirt. The height was measured through a Seca1 stadiometer from the feet to the vertex (Frankfort plane) [44]. Circumferences. Arm and leg circumferences, humeral and femoral diameters, and skin folds were evaluated with the FAGA SLR1 anthropometric kit. The circumference of the right leg was evaluated in this segment’s bulkiest area, in a standing position and with the gas- trocnemius relaxed; in contrast, the circumference of the right arm was evaluated in the bulki- est area of the contracted biceps; this evaluation was performed standing with the elbow in front and bent at 90 [43]. Diameters. The humeral epicondyle distance was considered the humerus’s diameter, which is the distance between the epicondyle and the right arm’s epitrochlea. For this evalua- tion, participants were standing with the elbow bent at 90˚. The distance between the femoral condyles (medial and distal) was considered the femur’s diameter, which evaluation was per- formed in a sitting position with the right knee bent at 90˚ [43, 44]. Skinfold thickness. Four skinfolds were considered to determine the participants’ somatotype: tricipital, subscapular, supra-iliac, and mid-calf [43–45]. Body Mass Index (BMI). The BMI’s interpretation was made according to anthropomet- ric standards to evaluate nutritional status [46]. Percentage of fat (%). The fat percentage was evaluated through impedance measurement with the Tanita Inner Scan BC-5541 digital scale. Waist-Hip Index (WHI). The WHI was obtained by dividing the waist perimeter, mea- sured at a point equidistant from the lower edge of the last rib and the iliac crest, by the perim- eter of the hips, measured at the greatest prominence of the buttocks [44, 47]. 12 weeks without mandatory physical training In regular class periods, the naval cadets had an average of two hours of daily mandatory phys- ical training (Monday through Saturday). This physical training was mandatory and consid- ered loads with the orientation to all physical capacities (strength, power, flexibility, speed, aerobic capacity and aerobic power). However, upon leaving school, whether for vacation or unplanned situations such as the current COVID 19 pandemic [30], the physical training regi- men was not mandatory. During the 12 weeks without mandatory physical training, the naval cadets voluntarily took part in walking, cycling, and ball games, among other activities. Standardized warm-up For both the first and the second evaluation of the 12MRT, the warm-up consisted of 10 min- utes of jogging, then 5 minutes of ballistic movements of the lower limb (adduction, abduction, PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 4 / 15 flexion, and extension of hips, and flexion and extension of knees and ankles). To finish, par- ticipants performed three 80-meter accelerations. After this warm-up and before running the 12MRT, there was a 5-minute break. 12-minute race test The evaluation of the 12MRT was carried out on a 400-meter athletic track. Before the evalua- tion, participants were instructed to perform as much distance as possible within the test’s 12 minutes. During the application of the test, the participants received verbal incentives from the researchers. The distance achieved in meters was converted into kilometers, and then the VO2 max was obtained through the following equation [48]: VO2 max ðmLO2
kgthe tricipital skinfold participants, a very large, negative correlation was observed between both variables (r = -0.76, p = 0.01). At the end of the 12 weeks without mandatory physical training (post-test), a very large, negative correlation was observed between VO2 max and the participants’ tricipital skinfold (r = -0.81, p = 0.01). The graphic representation of these analy- ses is presented in Fig 4. Discussion Concerning this study’s primary objective, the variables of VO2 max and anthropometric parameters showed changes after the 12 weeks without mandatory physical training in naval cadets from 18 to 25 years old. The findings revealed that the analysis initial point relates phys- ical training to quality of life [6, 8] and sports performance [1–3]. In this way, detrimental physiological changes and a decline in performance observed after a period without physical training can be reversed by applying correct training loads and professional supervision [17]. Specifically, the present study’s findings showed a significant decrease in the VO2 max of naval cadets, both men and women, after 12 weeks without mandatory physical training (p < 0.001, ES = 0.34). Similarly, Liguori et al. [37] determined changes in VO2 max after a vacation period without mandatory training; at the end of the vacation period, the researchers reported signifi- cant decreases in relative (p = 0.009) and absolute (p = 0.001) VO2 max in both men and women. Likewise, Sotiropoulos et al. [33] evaluated changes in VO2 max after a four-week transition period in soccer players. The experimental group (EG) conducted a directed Table 2. Mean values and SD before and after 12 weeks without mandatory physical training (n = 38). Test mean ± SD Post test mean ± SD Related differences Mean SD SEM 95% confidence interval t p d Lower Upper Weight (kg) 67.1 ± 8.0 67.5 ± 8.3 -0.32 1.78 0.28 -0.91 0.25 -1.13 ns 0.01 BMI (kg/m2) 22.5 ± 1.7 22.7 ± 1.8 -0.16 0.58 0.09 -0.35 0.02 -1.78 ns 0.10 % Fat 14.9 ± 5.2 14.9 ± 5.4 0.05 1.24 0.2 -0.35 0.46 0.26 ns 0.01 WHI 0.84 ± 0.05 0.83 ± 0.04 0.00 0.03 0.00 0.00 0.01 0.71 ns 0.08 WHeI 0.46 ± 0.03 0.46 ± 0.02 0.00 0.01 0.00 0.00 0.00 0.84 ns 0.08 Tricipital skinfold (mm) 11.1 ± 3.9 11.8 ± 4.0 -0.69 1.83 0.29 -1.29 -0.09 -2.34 ns 0.18 Subscapular skinfold (mm) 10.7 ± 3.1 10.9 ± 3.0 -0.26 1.32 0.21 -0.7 0.17 -1.22 ns 0.09 Suprailiac skinfold (mm) 9.4 ± 3.4 10.4 ± 3.8 -0.97 3.02 0.49 -1.97 0.01 -1.99 ns 0.27 Mid-calf skinfold (mm) 10.2 ± 4.6 9.9 ± 3.6 0.30 2.34 0.37 -0.46 1.07 0.79 ns 0.07 Arm circumference (cm) 31.6 ± 2.9 31.8 ± 3.0 -0.16 1.62 0.26 -0.7 0.36 -0.62 ns 0.06 Leg circumference (cm) 36.7 ± 2.0 36.8 ± 2.1 -0.11 0.77 0.12 -0.37 0.13 -0.91 ns 0.06 Humerus diameter 6.77 ± 0.42 6.76 ± 0.40 0.00 0.15 0.02 -0.04 0.05 0.21 ns 0.01 Femur diameter 9.76 ± 0.53 9.69 ± 0.52 0.06 0.17 0.02 0.01 0.12 2.4 ns 0.13 Endomorphic component 3.12 ± 0.96 3.32 ± 1.00 -0.20 0.55 0.08 -0.38 -0.02 -2.32 ns 0.21 Mesomorphic component 5.07 ± 0.96 5.10 ± 0.93 -0.02 0.39 0.06 -0.15 0.10 -0.41 ns 0.03 Ectomorphic component 2.51 ± 0.76 2.44 ± 0.77 0.06 0.27 0.04 -0.02 0.15 1.39 ns 0.08 12MRT (m) 3100.8 ± 348.6 2978.1 ± 364.7 122 115 18.6 84.9 160.5 6.57  0.34 VO2 max (mLO2
kg–1
min–1) 56.6 ± 6.9 54.2 ± 7.2 2.45 2.3 0.37 1.69 3.21 6.57  0.34 SD: standard deviation; SEM: standard error of the mean; WHI: waist-hip index; WHeI: waist-height index; BMI: muscle mass index; kg/m2: kilograms per square meters; 12MRT: 12-minute race test; mm: millimeters; cm: centimeters; m: meters; VO2 max: maximum oxygen consumption; mLO2
kg–1
min–1: milliliters of oxygen per kilogram of body mass per minute  p < 0.002; ns: not significant; d: Cohen’s d. https://doi.org/10.1371/journal.pone.0251516.t002 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 6 / 15 training program, while the control group (CG) executed a free training program. At the end of the research, the EG decreased from 57.66 ± 2.56 to 56.85 ± 2.52 mLO2
kg-1
min-1. In con- trast, the CG decreased from 58.08 ± 2.60 to 54.52 ± 2.80 mLO2
kg-1
min-1. Additionally, the researchers reported significant decreases in VO2 max when comparing the EG to the CG in the post-test (t = 16.06; p < 0.0001). Likewise, the endomorphic somatotype has a greater fat mass than the mesomorphic and ectomorphic somatotype [43], and subjects with endomor- phic predominance have shown a lower VO2 max than subjects with a mesomorphic or ectomorphic predominance (endomorphic: 37.3 ± 0.77; mesomorphic: 40.2 ± 0.46; and ecto- morphic: 43.5 ± 0.52) [51]. For this reason, the increase in the endomorphic component observed in naval cadets after 12 weeks without mandatory physical training could condition the decrease of VO2 max at the end of this period (p < 0.001, TE = 0.34). However, it is impor- tant to analyze the ES for each variable studied, which allows us to observe each phenomenon’s degree of presence, independent of the alpha level calculated [52]. In this study, like in research by Parpa & Michaelides [24], all ES in the tests with significant differences in VO2 max, includ- ing men and all data analysis, oscillated between 0.2–0.6. This was considered a small effect. Fig 2. Changes in VO2 max and anthropometric parameters before and after 12 weeks without mandatory physical training. 12MRT: 12-minute race test; mLO2
Kg–1
min–1: milliliters of oxygen per kilogram of body mass per minute; mm: millimeters; cm: centimeters; kg: kilograms; : p < 0.002. https://doi.org/10.1371/journal.pone.0251516.g002 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 7 / 15 On the other hand, the significant differences in women had an ES between 0.6–1.2 (which was considered as a moderate effect). Furthermore, the large and negative correlation between VO2 max and the fat percentage observed in the test (r = -0.69, p = 0.01) increased after the period without mandatory physical training (r = -0.75, p = 0.01). Up to this point, the decrease in VO2 max has been attributed to two leading causes; on the one hand, a transition period without mandatory and controlled physical training, while on the other hand, an increase in fat mass, reflected in the endomorphic component of naval cadets [51]. Periods without physical training have also been associated with a decrease in muscle cross- section [34]. This unfavorable consequence could be related to lower levels of muscle strength [35]. In this case, Koundourakis et al. [31] examined the effects of six weeks without physical training on performance parameters in soccer players; at the end of the study, the researchers reported significant decreases in both squat jump (Team A: 39.70 ± 3.32 vs 37.30 ± 3.08 kg; p < 0.001; Team B: 41.04 ± 3.34 vs 38.18 ± 3.03 kg; p < 0.001) and countermovement jump (Team A: 41.04 ± 3.99 vs 39.13 ± 3.26%; p < 0.001); Team B: 42.82 ± 3.60 vs 40.09 ± 2.79 kg; p < 0.001) in both experimental groups. The researchers also concluded that the observed reductions in jumping ability (considered to be a negative effect) could be related to mis- matches of rapidly contracting muscle fibers [25, 53]. In parallel, the endomorphic somatotype has lesser muscle mass than the mesomorphic and ectomorphic somatotype [43]. In turn, Mir- oshnichenko et al. [51] showed a high correlation between the predominance of the mesomor- phic component and VO2 max. Likewise, an increase in the endomorphic component and lower muscle mass could be associated with a lower VO2 max of the participants. Therefore, Fig 3. Somatotype before and after 12 weeks without mandatory physical training. https://doi.org/10.1371/journal.pone.0251516.g003 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 8 / 15 an increment of the endomorphic component in naval cadets may decrease the lower extremi- ties’ strength, generating biomechanical and neuronal changes [54]. These last changes could affect the economy of the race [55] and, consequently, decrease the performance in 12MRT (p < 0.001, ES = 0.34). Although the evidence shows the negative influence of periods without training on strength and muscular power [31, 35], mainly due to loss of muscle mass [34, 51], the present study did not consider assessing naval cadets’ anaerobic capacity. Therefore, the possible effects of 12 weeks without mandatory physical training on strength or power in both the lower and upper extremities should be considered in future studies. On the other hand, this study also showed increases in some anthropometric parameters after 12 weeks without mandatory physical training, specifically in the tricipital skinfold thick- ness in men (p = 0.02, ES = 0.18), arm circumference in women (p = 0.04, ES = 0.19) and the endomorphic component in both men and women (p = 0.02, ES = 0.25). In this sense, evi- dence shows that a period without physical training leads to increased fat mass and a decreased lean mass [31–33]. Also, the tricipital fold, together with the subscapular and suprailiac folds, are anthropometric indicators with a high explanatory power of VO2 max in both sexes [56]. We evidenced that those naval cadets with a higher tricipital fold had a reduced VO2 max Fig 4. Correlation between VO2 max and anthropometric parameters before and after 12 weeks without mandatory physical training. mLO2
Kg–1
min–1: milliliters of oxygen per kilogram of body mass per minute; % fat: percentage of fat; mm: millimeter. https://doi.org/10.1371/journal.pone.0251516.g004 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 9 / 15 (Test: r = 0.76, p = 0.01; post test: r = 0.81, p = 0.01). Likewise, an elevated tricipital fold condi- tions an elevated endomorphic component [42]. Consequently, anthropometric parameters influence cardiorespiratory fitness, independent of sex, age, and obesity level [57]. Related to this, Sotiropoulos et al. [33] evaluated changes in body weight and body fat percentage after a four-week transition period in soccer players (The EG conducted a directed training program and the CG a free training program). At the end of the study, the EG increased from 78.14 ± 4.77 to 78.74 ± 5.00 kg, while the CG increased from 76.48 ± 2.65 to 77.90 ± 2.82 kg (t = -4.91; p < 0.005); and, also reported increased percentage of body fat (EG from 7.92 ± 1.68 to 8.17 ± 1.81%; CG from 7.77 ± 1.79 to 8.59 ± 1.80%; t = -8.42; p < 0.005). On the other hand, Ormsbee et al. [58] examined the effect of five weeks without physical training on body com- position in swimmers. At the end of the study, significant differences were observed in body weight (68.96 ± 9.7 vs. 69.8 ± 9.8 kg; p = 0.03), fat mass (14.7 ± 7.6 vs. 16.5 ± 7.4 kg; p = 0.001), and waist circumference (72.7 ± 3.1 vs. 73.8 ± 3.6 cm; p = 0.03). Also, Koundourakis et al. [31] examined the effects of six weeks without physical training on the body composition of soccer players; at the end of the study, the researchers reported significant increases in both body weight (Team A: 77.60 ± 5.88 vs. 79.13 ± 6.16 kg; p < 0.001; Team B: 77.89 ± 8.75 vs. 79.49 ± 8.95 kg; p < 0.001) and in the fat percentage (Team A: 9.2 ± 3.33 vs. 11.01 ± 4.11%; p < 0.001; Team B: 9.43 ± 3.55 vs. 10.40 ± 4.08 kg; p < 0.001) in both experimental groups. Although some studies have established the body composition of armed forces personnel in some countries [59] and anthropometric changes have been documented concerning soldiers’ physical training [60], the effects of 12 weeks without mandatory physical training on anthro- pometric parameters have not been reported for naval cadets. Consequently, in connection with the studies referred to above, our study’s findings show the importance of verifying and controlling body composition after a period without mandatory physical training in naval cadets [61], especially somatotype indicators [43]. However, it is essential to mention that the present study did not control the participants’ caloric intake [62]. For this reason, we are not sure that the changes in anthropometric parameters were only due to a decrease in physical training [63–65]; there is a possibility that higher caloric intake, above the daily energy needs, has also influenced these physical changes [62, 66]. Finally, the data show that VO2 max is an essential parameter of the physical condition [38], and a higher VO2 max allows the efficient performance of physical tasks associated with military personnel [13, 60]. It has also been demonstrated that subjects with a higher percent- age of body fat have lower VO2 max, lower strength levels, and lower fatigue tolerance [67]. As demonstrated in this study, a vacation period without mandatory physical training generates decreases in the VO2 max [37] and negatively affects anthropometric parameters [51]. There- fore, the vacation periods must be adapted into a transition phase [24, 25]. In this way, with controlled and directed physical training, both athletes and naval cadets will have optimal physical recovery and maintenance; this condition will allow them to face better the next cycle of physical training [23]. One of the limitations of this study was the sample used. As mentioned above, the sample was by convenience, which would not allow us to generalize the data. However, armed forces personnel are more homogeneous in body structure [68] and eating behavior [69]. For this reason, in this specific case, the results could be generalized to this population. Conclusions Twelve weeks without mandatory physical training significantly decreases the VO2 max in naval cadets from 18 to 25 years old. Simultaneously, the same period without mandatory training increases skinfold thickness and the endomorphic component in this population. PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 10 / 15 Practical applications After evidence of decreases in VO2 max and negative increases in some anthropometric parameters after 12 weeks without mandatory physical training, it is suggested that training loads in the transition phase [25], whether due to vacations [28] or to unforeseen events [30]. Acknowledgments We thank the 38 naval cadets for their voluntary and disinterested participation in the Arturo Prat Naval Academy. Author Contributions Conceptualization: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Data curation: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Formal analysis: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Funding acquisition: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Investigation: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Methodology: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Resources: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Supervision: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Validation: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Visualization: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Galdames Maliqueo. Writing – original draft: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Gal- dames Maliqueo. Writing – review & editing: A´lvaro Huerta Ojeda, Guillermo Barahona-Fuentes, Sergio Gal- dames Maliqueo. References 1. Ojeda A´ H, Rı´os LC, Barrilao RG, Rios IC, Serrano PC. Effect of variable resistance on post-activation potentiation: A systematic review. Arch Med Deporte. 2016; 33(5):338–45. 2. Barahona-Fuentes G, Huerta Ojeda A´ , Galdames Maliqueo S. The influence of High-Intensity Interval Training Based Plyometric Exercise on Jump Height and Peak Power of Under-17 Male Soccer Players. Educacio´n Fı´sica y Ciencia. 2019; 21(2):e080. https://doi.org/10.24215/23142561e080. 3. Huerta A´ C, Galdames S, Cataldo M, Barahona G, Rozas T, Ca´ceres P. Effects of a high intensity inter- val training on the aerobic capacity of adolescents. Rev Med Chil. 2017; 145(8):972–9. https://doi.org/ 10.4067/s0034-98872017000800972 PMID: 29189854 4. Gacesa JZ, Jakovljevic DG, Kozic DB, Dragnic NR, Brodie DA, Grujic NG. Morpho-functional response of the elbow extensor muscles to twelve-week self-perceived maximal resistance training. Clin Physiol Funct Imaging. 2010; 30(6):413–9. https://doi.org/10.1111/j.1475-097X.2010.00957.x PMID: 20670339 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 11 / 15 5. Ingle L, Stephenson A, Sandercock GR. Physical activity profiles and selected muscular fitness vari- ables in English schoolchildren: A north–south divide? Eur J Sport Sci. 2016; 16(8):1187–96. https:// doi.org/10.1080/17461391.2016.1183714 PMID: 27220086 6. Sabido R, Peñaranda M, Herna´ndez-Davo´ JL. Comparison of acute responses to four different hyper- trophy-oriented resistance training methodologies. Eur J Hum Mov. 2016;37109–21. 7. Cordova A, Villa G, Sureda A, Rodriguez-Marroyo JA, Sa´nchez-Collado MP. Physical Activity and Car- diovascular Risk Factors in Spanish Children Aged 11–13 Years. Rev Española Cardiol. 2012; 65 (7):620–6. https://doi.org/10.1016/j.recesp.2012.01.026 PMID: 22633280 8. Ga´lvez Casas A, Rodrı´guez Garcı´a PL, Garcı´a-Canto´ E, Rosa Guillamo´n A, Pe´rez-Soto JJ, Tarraga Marcos L, et al. Aerobic capacity and quality of life in school children from 8 to 12. Clin Investig Arterios- cler. 2015; 27(5):239–45. https://doi.org/10.1016/j.arteri.2015.01.001 PMID: 25814171 9. Patel A V., Hodge JM, Rees-Punia E, Teras LR, Campbell PT, Gapstur SM. Relationship between mus- cle-strengthening activity and cause-specific mortality in a large US cohort. Prev Chronic Dis. 2020;171–9. https://doi.org/10.5888/pcd17.190408. 10. Ojeda A´ H, Rı´os LC, Barrilao RG, Serrano PC. Acute effect of a complex training protocol of back squats on 30-m sprint times of elite male military athletes. J Phys Ther Sci. 2016; 28(3):752–6. https://doi.org/ 10.1589/jpts.28.752 PMID: 27134353 11. Webber BJ, Flower AM, Pathak SR, Burganowski RP, Pawlak MT, Gottfredson RC, et al. Physical and Mental Health of US Air Force Military Training Instructors. Mil Med. 2019; 184(5–6):e248–54. https:// doi.org/10.1093/milmed/usy418 PMID: 30690457 12. Vrijkotte S, Roelands B, Pattyn N, Meeusen R. The Overtraining Syndrome in Soldiers: Insights from the Sports Domain. Mil Med. 2019; 184(5–6):e192–200. https://doi.org/10.1093/milmed/usy274 PMID: 30535270 13. Taylor MK, Markham AE, Reis JP, Padilla GA, Potterat EG, Drummond SPA, et al. Physical fitness influences stress reactions to extreme military training. Mil Med. 2008; 173(8):738–42. https://doi.org/ 10.7205/milmed.173.8.738 PMID: 18751589 14. Schneider KL, Spring B, Pagoto SL. Exercise and energy intake in overweight, sedentary individuals. Eat Behav. 2009; 10(1):29–35. https://doi.org/10.1016/j.eatbeh.2008.10.009 PMID: 19171314 15. Lovell DI, Cuneo R, Wallace J, McLellan C. The hormonal response of older men to sub-maximum aero- bic exercise: The effect of training and detraining. Steroids. 2012; 77(5):413–8. https://doi.org/10.1016/ j.steroids.2011.12.022 PMID: 22248672 16. Stebbings GK, Morse CI, McMahon GE, Onambele GL. Resting arterial diameter and blood flow changes with resistance training and detraining in healthy young individuals. J Athl Train. 2013; 48 (2):209–19. https://doi.org/10.4085/1062-6050-48.1.17 PMID: 23672385 17. Mujika I. The influence of training characteristics and tapering on the adaptation in highly trained individ- uals: A review. Int J Sports Med. 1998; 19(7):439–46. https://doi.org/10.1055/s-2007-971942 PMID: 9839839 18. Flann KL, Lastayo PC, McClain DA, Hazel M, Lindstedt SL. Muscle damage and muscle remodeling: No pain, no gain? J Exp Biol. 2011; 214(4):674–9. https://doi.org/10.1242/jeb.050112. 19. Gunter K, Baxter-Jones ADG, Mirwald RL, Almstedt H, Fuchs RK, Durski S, et al. Impact exercise increases BMC during growth: an 8-year longitudinal study. J Bone Miner Res. 2008; 23(7):986–93. https://doi.org/10.1359/jbmr.071201 PMID: 18072874 20. Maroto-Izquierdo S, Garcı´a-Lo´pez D, Fernandez-Gonzalo R, Moreira OC, Gonza´lez-Gallego J, de Paz JA. Skeletal muscle functional and structural adaptations after eccentric overload flywheel resistance training: a systematic review and meta-analysis. J Sci Med Sport. 2017; 20(10):943–51. https://doi.org/ 10.1016/j.jsams.2017.03.004 PMID: 28385560 21. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med Sci Sports Exerc. 2011; 43(7):1334– 59. https://doi.org/10.1249/MSS.0b013e318213fefb PMID: 21694556 22. Barahona-Fuentes G, Ojeda A´ H, Jerez-Mayorga D. Effects of different methods of strength training on indicators of muscle fatigue during and after strength training: a systematic review. Motriz J Phys Educ. 2020; 26(3):e10200063. https://doi.org/10.1590/S1980-6574202000030063. 23. Silva JR, Brito J, Akenhead R, Nassis GP. The transition period in soccer: a window of opportunity. Sport Med. 2016; 46(3):305–13. https://doi.org/10.1007/s40279-015-0419-3 PMID: 26530720 24. Parpa K, Michaelides MA. The effect of transition period on performance parameters in elite female soc- cer players. Int J Sports Med. 2020; 41(8):528–32. https://doi.org/10.1055/a-1103-2038 PMID: 32059247 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 12 / 15 25. Mujika I, Padilla S. Detraining: loss of training-induced physiological and performance adaptations. Part I. Sport Med. 2000; 30(2):79–87. https://doi.org/10.2165/00007256-200030020-00002. 26. Musumeci G. Sarcopenia and exercise “the state of the art.” J Funct Morphol Kinesiol. 2017; 2(4):. https://doi.org/10.3390/jfmk2040040. 27. Maugeri G, Castrogiovanni P, Battaglia G, Pippi R, D’Agata V, Palma A, et al. The impact of physical activity on psychological health during Covid-19 pandemic in Italy. Heliyon. 2020; 6(6):e04315. https:// doi.org/10.1016/j.heliyon.2020.e04315 PMID: 32613133 28. Katzmarzyk PT, Barreira T V., Broyles ST, Champagne CM, Chaput JP, Fogelholm M, et al. Physical activity, sedentary time, and obesity in an international sample of children. Med Sci Sports Exerc. 2015; 47(10):2062–9. https://doi.org/10.1249/MSS.0000000000000649 PMID: 25751770 29. Prentice-Dunn H, Prentice-Dunn S. Physical activity, sedentary behavior, and childhood obesity: a review of cross-sectional studies. Psychol Health Med. 2012; 17(3):255–73. https://doi.org/10.1080/ 13548506.2011.608806 PMID: 21995842 30. Paoli A, Musumeci G. Elite athletes and COVID-19 lockdown: future health concerns for an entire sec- tor. J Funct Morphol Kinesiol. 2020; 5(2):10–2. https://doi.org/10.3390/jfmk5020030 PMID: 33467246 31. Koundourakis NE, Androulakis NE, Malliaraki N, Tsatsanis C, Venihaki M, Margioris AN. Discrepancy between exercise performance, body composition, and sex steroid response after a six-week detraining period in professional soccer players. PLoS One. 2014; 9(2):. https://doi.org/10.1371/journal.pone. 0087803. 32. Reinke S, Karhausen T, Doehner W, Taylor W, Hottenrott K, Duda GN, et al. The influence of recovery and training phases on body composition, peripheral vascular function and immune system of profes- sional soccer players. PLoS One. 2009; 4(3):1–7. https://doi.org/10.1371/journal.pone.0004910. 33. Sotiropoulos AS, Travlos AK, Gissis I, Souglis AG, Grezios A. The effect of a 4-week training regimen on body fat and aerobic capacity of professional soccer players during the transition period. J Strength Cond Res. 2009; 23(6):1697–703. https://doi.org/10.1519/JSC.0b013e3181b3df69 PMID: 19675494 34. Hakkinen K. Effect of combined concentric and eccentric strength training and detraining on force-time, muscle fiber and metabolic characteristics of leg extensor muscles. Scand J Sport Sci. 1981;350–8. 35. Izquierdo M, Ibanez J, Gonzalez-Badillo JJ, Ratamess N, Kraemer WJ, Hakkinen K, et al. Detraining and tapering effects on hormonal responses and strength performance. J Strenght Cond Res. 2007; 21 (3):768–75. https://doi.org/10.1519/R-21136.1 PMID: 17685721 36. De Paiva PRV, Casalechi HL, Tomazoni SS, MacHado CDSM, Ribeiro NF, Pereira AL, et al. Does the combination of photobiomodulation therapy (PBMT) and static magnetic fields (sMF) potentiate the effects of aerobic endurance training and decrease the loss of performance during detraining? A rando- mised, triple-blinded, placebo-controlled trial. BMC Sports Sci Med Rehabil. 2020; 12(1):1–11. https:// doi.org/10.1186/s13102-020-00171-2. 37. Liguori G, Krebsbach K, Schuna J. Decreases in maximal oxygen uptake among army reserve officers’ training corps cadets following three months without mandatory physical training. Int J Exerc Sci. 2012; 5(4):354–9. PMID: 27182392 38. Ojeda A´ H, Maliqueo SG, Serrano PC. Validacio´n del test de 6 minutos de carrera como predictor del consumo ma´ximo de oxı´geno en el personal naval. Rev Cuba Med Mil. 2017; 46(4):1–11. 39. Galdames S, Huerta A´ , Pastene A. Effect of acute sodium bicarbonate supplementation on perfor- mance on the obstacle run in professional military pentathlete. Arch Med Deporte. 2020; 37(4):220–6. 40. Armada-de-Chile. ¿Quie´nes la componen? Armada de Chile. 2014; 41. Ato M, Lo´pez JJ, Benavente A. A classification system for research designs in psychology. An Psicol. 2013; 29(3):1038–59. https://doi.org/10.6018/analesps.29.3.178511. 42. Berral F. Protocolo de medidas antropome´tricas. Jornadas Me´dico Sanit. sobre Atlet., 2004;, p. 115– 22. 43. Carter JEL, Heath BH. Somatotyping: development and applications. vol. 5. New York, USA: Cam- bridge university press; 1990; 44. National Health and Nutrition Examination Survey. Anthropometry procedures manual. 2005; 45. Durnin J, Womersley J. Body fat assessed from total body density and its estimation from skinfold thick- ness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr. 1974;3277–97. https://doi.org/10.1079/bjn19740060 PMID: 4843734 46. Barrera A, Gladys M. Esta´ndares antropome´tricos para evaluacio´n del estado nutritivo. INTA. Santi- ago, Chile: 2004; 47. Mederico M, Paoli M, Zerpa Y, Briceño Y, Go´mez-Pe´rez R, Martı´nez JL, et al. Reference values of waist circumference and waist/hip ratio in children and adolescents of Me´rida, Venezuela: comparison PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 13 / 15 with international references. Endocrinol y Nutr. 2013; 60(5):235–42. https://doi.org/10.1016/j.endoen. 2012.12.006. 48. Cooper KH. A means of assessing maximal oxygen intake: correlation between field and treadmill test- ing. Jama. 1968; 203(3):201–4. PMID: 5694044 49. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009; 41(1):3–12. https://doi.org/10.1249/MSS. 0b013e31818cb278 PMID: 19092709 50. Martı´nez Camblor P. P-value adjustment for multiple comparisons. Rev Chil Salud Pu´blica. 2012; 16 (3):225–32. https://doi.org/10.5354/0717-3652.2012.23127. 51. Miroshnichenko VM, Furman YM, Brezdeniuk OY, Onyshchuk VE, Gavrylova N V, Salnykova S-V. Cor- relation of maximum oxygen consumption with component composition of the body, body mass of men with different somatotypes aged 25–35. Pedagog Phys Cult Sport. 2020; 24(2):290–6. https://doi.org/ 10.15561/26649837.2020.0603. 52. Casado A, Prieto L, Alonso J. El tamaño del efecto de la diferencia entre dos medias: ¿estadı´stica- mente significativo o clı´nicamente relevante? Med Clin (Barc). 1999; 112(15):584–8. PMID: 10365387 53. Perez-Gomez J, Rodriguez GV, Ara I, Olmedillas H, Chavarren J, Gonza´lez-Henriquez JJ, et al. Role of muscle mass on sprint performance: gender differences? Eur J Appl Physiol. 2008; 102(6):685–94. https://doi.org/10.1007/s00421-007-0648-8 PMID: 18084774 54. Trowell D, Vicenzino B, Saunders N, Fox A, Bonacci J. Effect of strength training on biomechanical and neuromuscular variables in distance runners: a systematic review and meta-analysis. Sport Med. 2020; 50(1):133–50. https://doi.org/10.1007/s40279-019-01184-9 PMID: 31541409 55. Skovgaard C, Christensen PM, Larsen S, Andersen TR, Thomassen M, Bangsbo J. Concurrent speed endurance and resistance training improves performance, running economy, and muscle NHE1 in mod- erately trained runners. J Appl Physiol. 2014; 117(10):1097–109. https://doi.org/10.1152/japplphysiol. 01226.2013 PMID: 25190744 56. de Andrade E, Gimenes H, Santos D. Which body fat anthropometric indicators are most strongly asso- ciated with maximum oxygen uptake in adolescents? Asian J Sports Med. 2017; 8(3):e13812. https:// doi.org/10.5812/asjsm.13812. 57. Yanek LR, Vaidya D, Kral BG, Dobrosielski DA, Moy TF, Stewart KJ, et al. Lean mass and fat mass as contributors to physical fitness in an overweight and obese african american population. Ethn Dis. 2015; 25(2):214–9. PMID: 26118151 58. Ormsbee MJ, Arciero P. Detraining increases body fat and weight and decreases VO2peak and meta- bolic rate. J Strength Cond Res. 2012; 26(8):2087–95. https://doi.org/10.1519/JSC. 0b013e31823b874c PMID: 22027854 59. Nelson R, Cheatham J, Gallagher D, Bigelman K, Thomas DM. Revisiting the United States Army body composition standards: a receiver operating characteristic analysis. Int J Obes. 2019; 43(8):1508–15. https://doi.org/10.1038/s41366-018-0195-x PMID: 30181655 60. Dyrstad SM, Soltvedt R, Halle´n J. Physical fitness and physical training during norwegian military ser- vice. Mil Med. 2006; 171(8):736–41. https://doi.org/10.7205/milmed.171.8.736 PMID: 16933814 61. Pl¸avin¸a L, Umbrasˇko S. Analysis of physical fitness tests and the body composition of the military per- sonnel. Pap Anthropol. 2016; 25(1):27. https://doi.org/10.12697/poa.2016.25.1.03. 62. Thomas DT, Erdman KA, Burke LM. Position of the academy of nutrition and dietetics, dietitians of can- ada, and the american college of sports medicine: nutrition and athletic performance. J Acad Nutr Diet. 2016; 116(3):501–28. https://doi.org/10.1016/j.jand.2015.12.006 PMID: 26920240 63. Franckle R, Adler R, Davison K. Accelerated weight gain among children during summer versus school year and related racial/ethnic disparities: a systematic review. Prev Chronic Dis. 2014; 11(12):1–10. https://doi.org/10.5888/pcd11.130355 PMID: 24921899 64. Moreno JP, Johnston CA, Woehler D. Changes in weight over the school year and summer vacation: results of a 5-year longitudinal study. J Sch Health. 2013; 83(7):473–7. https://doi.org/10.1111/josh. 12054 PMID: 23782089 65. von Hippel PT, Workman J. From kindergarten through second grade, U.S. children’s obesity preva- lence grows only during summer vacations. Obesity. 2016; 24(11):2296–300. https://doi.org/10.1002/ oby.21613 PMID: 27804271 66. Cooper JA, Tokar T. A prospective study on vacation weight gain in adults. Physiol Behav. 2016;15643–7. https://doi.org/10.1016/j.physbeh.2015.12.028 PMID: 26768234 67. Crawford K, Fleishman K, Abt JP, Sell TC, Lovalekar M, Nagai T, et al. Less body fat improves physical and physiological performance in army soldiers. Mil Med. 2011; 176(1):35–43. https://doi.org/10.7205/ milmed-d-10-00003 PMID: 21305957 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 14 / 15 68. Maldonado I, Calero S. Anthropometric profile and body composition in aspirants of the Army Soldiers Training School. Rev Cuba Investig Biome´dicas. 2017; 36(2):1–13. 69. Kildal CL, Syse KL. Meat and masculinity in the Norwegian Armed Forces. Appetite. 2017; 112(Novem- ber 2013):69–77. https://doi.org/10.1016/j.appet.2016.12.032 PMID: 28040506 PLOS ONE Period without physical training on maximum oxygen uptake PLOS ONE | https://doi.org/10.1371/journal.pone.0251516 June 2, 2021 15 / 15 (...TRUNCATED)

Effects of a period without mandatory physical training on maximum oxygen uptake and anthropometric parameters in naval cadets.(...TRUNCATED)

06-02-2021

Huerta Ojeda, Álvaro,Barahona-Fuentes, Guillermo,Galdames Maliqueo, Sergio(...TRUNCATED)

eng

PMC10703220

Reference number: PONE-D-23-18858 (previous submission PONE-D-23-16206) Exploring running styles in the field through cadence and duty factor modulation Dear dr. L. A. Peyré-Tartaruga and editorial office, Thank you for evaluating our manuscript and for giving us the opportunity to resubmit. We have made the following changes to the manuscript in response to the concerns of the editorial office: - We have included the reference numbers of both the original ethical application (VCWE-2019– 006R1) and the amendment (VCWE-2021-043) in the method section of the manuscript. - We have included the original ethical application (VCWE-2019–006R1) in Dutch, with the English translations in comments in the pdf file. - We have included the amendment (VCWE-2021-043) in Dutch, with the English translations in comments in the pdf file. - We have included the approval emails from our IRB for both the original ethical application (VCWE-2019–006R1) and the amendment (VCWE-2021-043). - We have included the informed consent form for this study in English. - We have included the participant information form for this study in English. We believe that by making those changes and including the additional files we have addressed the concerns of the editorial office. Please find below the message from the editorial office. Thank you for your time and consideration. Sincerely, Anouk Nijs, Msc. a.nijs@vu.nl Dr. Melvyn Roerdink m.roerdink@vu.nl Prof. Dr. Peter J. Beek p.j.beek@vu.nl Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, The Netherlands PONE-D-23-16206 Exploring running styles in the field through cadence and duty factor modulation PLOS ONE Dear Dr. Nijs, I am writing to you about your appeal on the editorial decision for your submission to PLOS ONE above. After careful consideration of the manuscript, the reasons for the previous rejection, and your reasons for appealing, we are upholding the decision to reject the manuscript. As you are aware, manuscripts submitted to PLOS ONE are assessed based on the journal’s publication criteria. We have concerns on the contents of the manuscript including that the approval document provided did not match the study presented in the manuscript. Furthermore, the approval number on the manuscript (VCWE-2019–006R1) did not match the one provided in the email from your IRB (VCWE- 2021-043) Considering those concerns, the manuscript does not currently meet our criteria for publication requiring that the research meets all applicable standards for the ethics of experimentation and research integrity. However, if you are able to provide a copy of the original approval document issued by your IRB (i.e. VCWE-2019–006R1) and an English translation, as well as the documents that you have mentioned in your appeal email, we do feel that a revised manuscript may be suitable for consideration. This would however need to be considered as a new submission. If you are able to revise the manuscript as indicated above and submit a new manuscript to PLOS ONE, please refer to the original submission in the cover letter. Thank you for your interest in PLOS ONE. Best wishes, Anushmathi PM Editorial Office PLOS ONE (...TRUNCATED)

Exploring running styles in the field through cadence and duty factor modulation.(...TRUNCATED)

12-07-2023

Nijs, Anouk,Roerdink, Melvyn,Beek, Peter Jan(...TRUNCATED)

eng

PMC7309010

sensors Article Effects of Novel Inverted Rocker Orthoses for First Metatarsophalangeal Joint on Gastrocnemius Muscle Electromyographic Activity during Running: A Cross-Sectional Pilot Study Rubén Sánchez-Gómez 1 , Carlos Romero-Morales 2,* , Álvaro Gómez-Carrión 1, Blanca De-la-Cruz-Torres 3 , Ignacio Zaragoza-García 1 , Pekka Anttila 4, Matti Kantola 4 and Ismael Ortuño-Soriano 1 1 Nursing Department, Faculty of Nursing, Physiotherapy and Podiatry, Universidad Complutense de Madrid, 28040 Madrid, Spain; rusanc02@ucm.es (R.S.-G.); alvaroalcore@hotmail.com (Á.G.-C.); izaragoz@ucm.es (I.Z.-G.); iortunos@ucm.es (I.O.-S.) 2 Faculty of Sport Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain 3 Department of Physiotherapy, University of Seville, c/Avicena, s/n, 41009 Seville, Spain; bcruz@us.es 4 Applied Science of Metropolia Univesity, Podiatry Department, 01600 Helsinki, Finland; pekka.anttila@metropolia.fi (P.A.); Matti.Kantola@metropolia.fi (M.K.) * Correspondence: carlos.romero@universidadeuropea.es Received: 15 April 2020; Accepted: 3 June 2020; Published: 5 June 2020



 Abstract: Background: The mobility of the first metatarsophalangeal joint (I MPTJ) has been related to the proper windlass mechanism and the triceps surae during the heel-off phase of running gait; the orthopedic treatment of the I MPTJ restriction has been made with typical Morton extension orthoses (TMEO). Nowadays it is unclear what effects TMEO or the novel inverted rocker orthoses (NIRO) have on the EMG activity of triceps surae during running. Objective: To compare the TMEO effects versus NIRO on EMG triceps surae on medialis and lateralis gastrocnemius activity during running. Study design: A cross-sectional pilot study. Methods: 21 healthy, recreational runners were enrolled in the present research (mean age 31.41 ± 4.33) to run on a treadmill at 9 km/h using aleatory NIRO of 6 mm, NIRO of 8 mm, TMEO of 6 mm, TMEO of 8 mm, and sports shoes only (SO), while the muscular EMG of medial and lateral gastrocnemius activity during 30 s was recorded. Statistical intraclass correlation coefficient (ICC) to test reliability was calculated and the Wilcoxon test of all five different situations were tested. Results: The reliability of values was almost perfect. Data showed that the gastrocnemius lateralis increased its EMG activity between SO vs. NIRO-8 mm (22.27 ± 2.51 vs. 25.96 ± 4.68 mV, p < 0.05) and SO vs. TMEO-6mm (22.27 ± 2.51 vs. 24.72 ± 5.08 mV, p < 0.05). Regarding gastrocnemius medialis, values showed an EMG notable increase in activity between SO vs. NIRO-6mm (22.93 ± 2.1 vs. 26.44 ± 3.63, p < 0.001), vs. NIRO-8mm (28.89 ± 3.6, p < 0.001), and vs. TMEO-6mm (25.12 ± 3.51, p < 0.05). Conclusions: Both TMEO and NIRO have shown an increased EMG of the lateralis and medialis gastrocnemius muscles activity during a full running cycle gait. Clinicians should take into account the present evidence when they want to treat I MTPJ restriction with orthoses, and consider the inherent triceps surae muscular cost relative to running economy. Keywords: triceps surae; first metatarsophalangeal joint; surface electromyography 1. Introduction Coterill [1] was the first author who described painful osteoarthritis (OA) of the first metatarsophalangeal joint (IMTPJ), which is known as hallux rigidus (HR). HR is the last stage Sensors 2020, 20, 3205; doi:10.3390/s20113205 www.mdpi.com/journal/sensors Sensors 2020, 20, 3205 2 of 12 of the IMTPJ degeneration, with functional hallux limitus [2] (FHL) at the beginning of the pathological progress [3]. Joint disease is thought to be caused by repetitive impacts on the dorsal aspect of the base of the proximal phalanx of the hallux by the first metatarsal head during the propulsion phase of gait and running in feet with multifactorial biomechanical and/or structural deficits [4]. The limitation of IMTPJ has been linked to gait problems [5] and its consequences on ankle, knee, hip, or low back during running [6]. The treatment of this injury has been addressed in several conservative non-surgical and surgical ways. Non-surgical management is valid to treat HR in the earliest stages [7,8] and includes ultrasound therapy, infiltrative drugs, shoe modifications, hallux bandages, manual mobilization, flexor strengthening, and orthoses to improve the joint problems. There are a few references on treatment of OA using plantar insoles in HR and FHL. Traditional Morton’s extensions are orthoses with a flat light modification under the first ray that has been used to treat HR [9–11] to avoid the impact between the proximal phalanx and first metatarsal bones. This opens the IMTPJ dorsally but restricts its dorsiflexion movement, while rocker-sole footwear modifications have shown a reduction in the peak pressure under the IMTPJ. This decreases the average gait cycle that is spent in the stance phase [12] and increases muscle activity of the lower limb [13]. However, there is no reference to either the inverted rocker-sole orthoses effects or the effect of footwear modifications on muscle activity during running. On the other hand, running economy (RE) has been described as the oxygen cost of running at a given speed in every case [14] and factors such as biomechanics and muscular fatigue can influence the RE [15]. Additionally, barefoot running has shown differences in biomechanical behaviour [16] and muscular responses [17,18] when it is compared with classical running shoes. Compared to fatigue, strength training added to a normal training program for distance running can improve RE between 2% and 8%. An increase in muscle mass training programs around the proximal region of the lower limb, such as quadriceps or hamstring [19], or around the distal regions, such as the triceps surae [20] with plantarflexion and dorsiflexion ankle exercises, has shown some benefits on RE. Accordingly, triceps surae and its relationship with the windlass mechanism [21] in the propulsion phase of gait and running has been reported to provide between 8% and 17% of the elastic energy that is needed for the heel-off phase [22,23] toward a suitable IMTPJ dorsiflexion [24,25]. However, the electromyography (EMG) effects in the triceps surae with limited dorsiflexion of the IMTPJ that is induced by any orthotic dorsiflexion restriction has never been studied. Understanding the EMG activity of this muscle will allow us to understand if the subjects could be increasing their energy cost during running, which is very important for an efficient RE [19]. However, no previous research has studied the effect of a novel inverted rocker orthoses (NIRO) on the EMG activity of the triceps surae compared to traditional Morton’s extension orthoses (TMEO) during running in the healthy population. Because of the restricting IMTPJ effect of TMEO and its influence on the windlass mechanism that is linked with the triceps surae [24,25], we hypothesized that TMEO (6 mm and 8 mm) may increase the EMG activity of the gastrocnemius medialis and lateralis muscles compared to the shoe only (SO) condition during running activity; in addition, regarding previous muscular activity changes that are reported with classical rocker soles [13], we hypothesized that NIRO (6 mm and 8 mm) may reduce the EMG of gastrocnemius medialis and lateralis compared to TMEO (6 mm and 8 mm), and this may increase EMG compared to SO in healthy people during running activity. 2. Materials and Methods The public institutional review board at Virgen Macarena-Virgen del Rocío hospitals, reviewed and approved the present study (certificate number f7f4a6567676d7ba7163bce0d15e7f98c9f33354). Ethical and human criteria were followed according to the Declaration of Helsinki, and signed informed consent was obtained from all subjects. Sensors 2020, 20, 3205 3 of 12 2.1. Design and Sample Size The statistics unit at the Spanish public university used software to assess the suitable sample size to perform this cross-sectional observational study and to study the difference in the EMG changes in the gastrocnemius medialis and lateralis muscles between SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, and TMEO 8 mm groups during running. Previous data on the triceps surae showed 7.0 ± 0.6 millivolts (mV) wearing 9-mm heel lifts compared to 4.9 ± 0.6 mV wearing typical shoes [26]. Taking into account a statistical power of 80%, β = 20%, a 95% confidence interval (CI), and α = 0.05, 30 subjects were needed to complete the study. Considering the typical loss of 20% subjects, 24 participants were recruited. However, three individuals were excluded from the study because they felt pain and discomfort during the EMG assessment. Reporting of Observational Studies in Epidemiology (STROBE) [27] criteria and a randomly consecutive sampling technique were followed to develop the present research. 2.2. Subjects The following inclusion criteria were used to select the participants: (1) healthy participants, between 18 and 30 years old; (2) recreational runners with 3–4 h of training per week with more than 1 year of experience; (3) neutral foot posture index (FPI) with values between 0 and +5 points according to a validity tool [28]; and (4) no injuries or pain at the time of the test. The exclusion criteria were as follows: (1) any lower limb injury during the last 6 months; (2) less movement in either foot joint than what is required to perform the optimal biomechanics according to normal values [29,30]; or (3) under the influence of any drugs effects at the time of the measurements. Body mass index (BMI) was taken into account to select a homogeneous sample, using Quetelet’s equation as follows: BMI = weight (kg)/height (m2) [31]. 2.3. Instrumentation and Assessments Neurotrac® Simplex Plus (Verity Medical Ltd., Braishfield, UK) EMG electronic device with a USB-Bluetooth [32] was used to study the triceps surae activity during the running test. The recording range on the device was 0.2 mV to 2000 mV, with a sensitivity of 0.1 mV RMS, 10 m of free wireless (Bluetooth) connection range and an accuracy of 4% of the reading from mV +/− 0.3 mV to 200 Hz, with a bandpass filter of 18 Hz +/− 4 Hz to 370 Hz +/− 10% for readings below 235 mV. The signal was assessed using self-adhesive circular surface electrodes that were 30 mm in diameter and made of high-quality hydrogel and conductive carbon film to detect the electrical action of the muscle fibers. The signal from each electrode was captured by the receiver module and filtered automatically by the Neurotrac® software (Verity Medical Ltd., Braishfield, UK). It was sent by a unidirectional radioelectric secure connection to the computer and it was digitally transformed by the software to generate activity patterns data for each electrode. 2.4. Materials NIRO was made using a flat sheet of ethylene-vinyl acetate (EVA) with a semi-rigid density that was 3 mm thick, without any orthotic element that could interface with normal biomechanical behaviour of the foot. NIRO had an inverted rocker composed of EVA medium that was 5 cm long, 2 cm wide, and 6 mm thick. Its proximal and distal edges were smoothly polished, and it was placed on the IMTPJ. The whole orthotic was covered with an EVA soft layer that was 1 mm thick (Figure 1). The TMEO was made with the same flat sheet of semi-rigid EVA that was 3 mm thick without any orthotic element and with a rectangular flat piece of EVA medium (6 mm thick) that was placed under the IMTPJ area and it was covered with an EVA soft layer that was 1 mm thick (Figure 2). The neutral SOs were “New Feel PW 100M medium grey” (ref. number: 2018022). NIRO and TMEO were made in an external orthopedic laboratory that was blinded to the study protocol. Sensors 2020, 20, 3205 4 of 12 Sensors 2020, 20, x FOR PEER REVIEW 4 of 12 Figure 1. Novel inverted rocker orthotic (NIRO). A flat sheet of ethylene-vinyl acetate (EVA) with an inverted rocker piece of EVA medium 6 mm thick under IMTPJ (bulked raised shape) covered with a yellow EVA soft layer that was 1 mm thick. Figure 2. Typical Morton’s extension orthotic (TMEO). A flat sheet of ethylene-vinyl acetate (EVA) with a rectangular flat piece of EVA medium 6 mm thick under IMTPJ covered with a black EVA soft layer that was 1 mm thick. 2.5. Procedure The podiatric clinician researcher (RSG) performed a physical assessment of the subjects and applied the eligibility criteria. To visualize the muscle belly, each subject was asked to perform plantarflexion of the ankle joint for a few seconds. The surface electrodes were then placed longitudinally onto the most prominent bulge of the gastrocnemius medialis and lateralis, based on the “European recommendations for surface EMG” [33]. The subjects were then asked to stand on one leg in the tip-toe position using their dominant foot for 5 s to set the maximal voluntary contractions that were needed in the strongest limb to calibrate the software and to normalize EMG data amplitudes for each test [34]. This was followed by acclimatization of subjects to a motorized treadmill at 5.17 km/h for 3 min [17]. The participants were divided randomly in gastrocnemius lateralis or medialis group by choosing a sealed envelope that assigned them to one group or another to begin the test; after that, they selected one of the five sealed envelopes with each of the five different conditions of the study (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, TMEO 8 mm) to set randomly the order of the test. The 11 subjects who began with medialis gastrocnemius assessments, did the lateralis test following the same randomized protocol for each of the five different conditions and vice versa for the 12 participants who began with the lateralis test (Figure 3). Three running trials at 9 km/h [35] under five different conditions (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, and TMEO 8 mm) on the same day were randomly performed. The duration of each trial was 1 min. For each subject, the mean EMG muscle activity pattern [36] of the gastrocnemius medialis of the dominant leg was recorded during the last 30 s of each 1-min trial, which was performed three times, leaving 5 min of rest between each test [37]. To avoid a potential imbalance, the same condition was added to contralateral foot. The same protocol was performed to Figure 1. Novel inverted rocker orthotic (NIRO). Sensors 2020, 20, x FOR PEER REVIEW 4 of 12 Figure 1. Novel inverted rocker orthotic (NIRO). A flat sheet of ethylene-vinyl acetate (EVA) with an inverted rocker piece of EVA medium 6 mm thick under IMTPJ (bulked raised shape) covered with a yellow EVA soft layer that was 1 mm thick. Figure 2. Typical Morton’s extension orthotic (TMEO). A flat sheet of ethylene-vinyl acetate (EVA) with a rectangular flat piece of EVA medium 6 mm thick under IMTPJ covered with a black EVA soft layer that was 1 mm thick. 2.5. Procedure The podiatric clinician researcher (RSG) performed a physical assessment of the subjects and applied the eligibility criteria. To visualize the muscle belly, each subject was asked to perform plantarflexion of the ankle joint for a few seconds. The surface electrodes were then placed longitudinally onto the most prominent bulge of the gastrocnemius medialis and lateralis, based on the “European recommendations for surface EMG” [33]. The subjects were then asked to stand on one leg in the tip-toe position using their dominant foot for 5 s to set the maximal voluntary contractions that were needed in the strongest limb to calibrate the software and to normalize EMG data amplitudes for each test [34]. This was followed by acclimatization of subjects to a motorized treadmill at 5.17 km/h for 3 min [17]. The participants were divided randomly in gastrocnemius lateralis or medialis group by choosing a sealed envelope that assigned them to one group or another to begin the test; after that, they selected one of the five sealed envelopes with each of the five different conditions of the study (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, TMEO 8 mm) to set randomly the order of the test. The 11 subjects who began with medialis gastrocnemius assessments, did the lateralis test following the same randomized protocol for each of the five different conditions and vice versa for the 12 participants who began with the lateralis test (Figure 3). Three running trials at 9 km/h [35] under five different conditions (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, and TMEO 8 mm) on the same day were randomly performed. The duration of each trial was 1 min. For each subject, the mean EMG muscle activity pattern [36] of the gastrocnemius medialis of the dominant leg was recorded during the last 30 s of each 1-min trial, which was performed three times, leaving 5 min of rest between each test [37]. To avoid a potential imbalance, the same condition was added to contralateral foot. The same protocol was performed to Figure 2. Typical Morton’s extension orthotic (TMEO). A flat sheet of ethylene-vinyl acetate (EVA) with an inverted rocker piece of EVA medium 6 mm thick under IMTPJ (bulked raised shape) covered with a yellow EVA soft layer that was 1 mm thick. A flat sheet of ethylene-vinyl acetate (EVA) with a rectangular flat piece of EVA medium 6 mm thick under IMTPJ covered with a black EVA soft layer that was 1 mm thick. 2.5. Procedure The podiatric clinician researcher (RSG) performed a physical assessment of the subjects and applied the eligibility criteria. To visualize the muscle belly, each subject was asked to perform plantarflexion of the ankle joint for a few seconds. The surface electrodes were then placed longitudinally onto the most prominent bulge of the gastrocnemius medialis and lateralis, based on the “European recommendations for surface EMG” [33]. The subjects were then asked to stand on one leg in the tip-toe position using their dominant foot for 5 s to set the maximal voluntary contractions that were needed in the strongest limb to calibrate the software and to normalize EMG data amplitudes for each test [34]. This was followed by acclimatization of subjects to a motorized treadmill at 5.17 km/h for 3 min [17]. The participants were divided randomly in gastrocnemius lateralis or medialis group by choosing a sealed envelope that assigned them to one group or another to begin the test; after that, they selected one of the five sealed envelopes with each of the five different conditions of the study (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, TMEO 8 mm) to set randomly the order of the test. The 11 subjects who began with medialis gastrocnemius assessments, did the lateralis test following the same randomized protocol for each of the five different conditions and vice versa for the 12 participants who began with the lateralis test (Figure 3). Three running trials at 9 km/h [35] under five different conditions (SO, NIRO 6 mm, NIRO 8 mm, TMEO 6 mm, and TMEO 8 mm) on the same day were randomly performed. The duration of each trial was 1 min. For each subject, the mean EMG muscle activity pattern [36] of the gastrocnemius medialis of the dominant leg was recorded during the last 30 s of each 1-min trial, which was performed three times, leaving 5 min of rest between each test [37]. To avoid a potential imbalance, the same condition was added to contralateral foot. The same protocol was performed to assess another gastrocnemius EMG activity pattern. Subjects were blinded to which of the five random conditions that they were wearing, and the results were used to test the hypothesis. Sensors 2020, 20, 3205 5 of 12 Sensors 2020, 20, x FOR PEER REVIEW 5 of 12 assess another gastrocnemius EMG activity pattern. Subjects were blinded to which of the five random conditions that they were wearing, and the results were used to test the hypothesis. Figure 3. Randomized flow chart. Abbreviations: SO = shoe only; NIRO = novel inverted rocker orthoses; and TMEO = traditional Morton extension´s orthoses. 2.6. Statistical Analysis To test for reliability in the present research, within-day trial-to-trial intraclass correlation coefficient (ICC) and the standard error of measurement (SEM) were calculated for the subjects under the five conditions for each muscle during the running test [14]. According to Landis and Koch [38], coefficients of ICC that were lower than 0.20 indicated a slight agreement, 0.20–0.40 indicated fair reliability, 0.41–0.60 indicated moderate reliability, 0.61–0.80 indicated substantial reliability, and 0.81–1.00 indicated almost perfect reliability. The authors considered coefficients of ≥0.81 to be appropriate to consider the results of the study as valid. SEM assessed the minimal detectable change (MDC) for all measurements. This is known as reliable change index (RCI), and it was used to determine the clinical significance of the data [39]. The Shapiro–Wilks test was used to assess the normality of the sample, and normal a distribution was present if p >0.05. Demographic values were presented as the mean and standard deviation (±SD). The p-values for multiple comparisons were corrected with a non-parametric paired Friedman test to prove that all SOs, NIROs, and TMEOs conditions were different between them. The Wilcoxon test with Bonferroni’s correction was performed to analyze differences between the five different conditions, indicating statistically significant differences when p < 0.05 with a 95% CI. All the values that were generated using NeuroTrac® software were loaded into Excel® template (Windows® 97–2003), and they were analyzed using SPSS version 19.0 (SPSS Science, Chicago, IL, USA). 3. Results The Shapiro–Wilks test showed a non-normal distribution of the sample (p < 0.05), while the Friedman test showed that values were different between the five conditions (p < 0.05). All subjects were recruited from a biomechanical clinic in Madrid (Spain) over a two-month period (October to Figure 3. Randomized flow chart. Abbreviations: SO = shoe only; NIRO = novel inverted rocker orthoses; and TMEO = traditional Morton extension’s orthoses. 2.6. Statistical Analysis To test for reliability in the present research, within-day trial-to-trial intraclass correlation coefficient (ICC) and the standard error of measurement (SEM) were calculated for the subjects under the five conditions for each muscle during the running test [14]. According to Landis and Koch [38], coefficients of ICC that were lower than 0.20 indicated a slight agreement, 0.20–0.40 indicated fair reliability, 0.41–0.60 indicated moderate reliability, 0.61–0.80 indicated substantial reliability, and 0.81–1.00 indicated almost perfect reliability. The authors considered coefficients of ≥0.81 to be appropriate to consider the results of the study as valid. SEM assessed the minimal detectable change (MDC) for all measurements. This is known as reliable change index (RCI), and it was used to determine the clinical significance of the data [39]. The Shapiro–Wilks test was used to assess the normality of the sample, and normal a distribution was present if p >0.05. Demographic values were presented as the mean and standard deviation (±SD). The p-values for multiple comparisons were corrected with a non-parametric paired Friedman test to prove that all SOs, NIROs, and TMEOs conditions were different between them. The Wilcoxon test with Bonferroni’s correction was performed to analyze differences between the five different conditions, indicating statistically significant differences when p < 0.05 with a 95% CI. All the values that were generated using NeuroTrac® software were loaded into Excel® template (Windows® 97–2003), and they were analyzed using SPSS version 19.0 (SPSS Science, Chicago, IL, USA). Sensors 2020, 20, 3205 6 of 12 3. Results The Shapiro–Wilks test showed a non-normal distribution of the sample (p < 0.05), while the Friedman test showed that values were different between the five conditions (p < 0.05). All subjects were recruited from a biomechanical clinic in Madrid (Spain) over a two-month period (October to November 2019). Forty-five subjects were asked to participate in the experiment and assessed for eligibility; 24 did not meet the study entry requirements and three withdrew from the study because of pain and discomfort. Ultimately, 21 participants (10 males and 11 females) were enrolled into the study. The participants’ flow chart following the STROBE guidelines, is shown in Figure 4. Sociodemographic data are shown in Table 1. Sensors 2020, 20, x FOR PEER REVIEW 6 of 12 November 2019). Forty-five subjects were asked to participate in the experiment and assessed for eligibility; 24 did not meet the study entry requirements and three withdrew from the study because of pain and discomfort. Ultimately, 21 participants (10 males and 11 females) were enrolled into the study. The participants’ flow chart following the STROBE guidelines, is shown in Figure 4. Sociodemographic data are shown in Table 1. Figure 4. Participant flow chart. Table 1. Participant demographics. Variable n = 21 Mean ± SD (95% CI) Age 31.41 ± 4.33 (32.26–35.09) FPI (scores) 3.12 ± 0.17 (2.07–3.41) Weight (kg) 67.50 ± 8.06 (62.36–70.06) Height (cm) 170.08 ± 6.91 (166.9–172.43) BMI (kg/m2) 23.15 ± 3.05 (21.7–24.7) Abbreviations: SD = standard deviation; CI = confidence interval; FPI = foot posture index; and BMI = body mass index. The reliability of the data obtained from the EMG activity of muscles during running under five different conditions is presented as the ICC and SEM, which are shown in Table 2. Most of the values reached cut-off values over of 0.81 in the ICC data, which suggests “almost perfect reliability” [38], with 0.971 for NIRO-8 mm as the highest value and 0.458 for TMEO-8 mm as the lowest for the gastrocnemius lateralis, and 0.894 for TMEO-8 mm as the highest and 0.767 for NIRO-8 mm as the lowest for the gastrocnemius medialis. Considering the reference that was chosen by the authors, we dismissed TMEO-8 mm values for gastrocnemius lateralis. For SEM, 0.817 mV was the lowest value set for NIRO-8 mm, and 3.766 mV was the lowest value for TMEO-6 mm for the gastrocnemius lateralis, and 2.083 mV was the highest value for NIRO-8 mm and 0.326 Figure 4. Participant flow chart. Table 1. Participant demographics. Variable n = 21 Mean ± SD (95% CI) Age 31.41 ± 4.33 (32.26–35.09) FPI (scores) 3.12 ± 0.17 (2.07–3.41) Weight (kg) 67.50 ± 8.06 (62.36–70.06) Height (cm) 170.08 ± 6.91 (166.9–172.43) BMI (kg/m2) 23.15 ± 3.05 (21.7–24.7) Abbreviations: SD = standard deviation; CI = confidence interval; FPI = foot posture index; and BMI = body mass index. Sensors 2020, 20, 3205 7 of 12 The reliability of the data obtained from the EMG activity of muscles during running under five different conditions is presented as the ICC and SEM, which are shown in Table 2. Most of the values reached cut-off values over of 0.81 in the ICC data, which suggests “almost perfect reliability” [38], with 0.971 for NIRO-8 mm as the highest value and 0.458 for TMEO-8 mm as the lowest for the gastrocnemius lateralis, and 0.894 for TMEO-8 mm as the highest and 0.767 for NIRO-8 mm as the lowest for the gastrocnemius medialis. Considering the reference that was chosen by the authors, we dismissed TMEO-8 mm values for gastrocnemius lateralis. For SEM, 0.817 mV was the lowest value set for NIRO-8 mm, and 3.766 mV was the lowest value for TMEO-6 mm for the gastrocnemius lateralis, and 2.083 mV was the highest value for NIRO-8 mm and 0.326 mV was the lowest value for TMEO-8 mm for the gastrocnemius medialis. The highest MDC value for TMEO-8 mm was 5.798 mV and 2.264 mV were the lowest value for the gastrocnemius lateralis. Additionally, 5.775 mV was the highest value in the NIRO-8 mm group and 0.904 mV was the lowest value in the TMEO-8 mm group for gastrocnemius medialis. EMG mean muscle activity in the gastrocnemius medialis and lateralis in SO compared to NIRO-6 mm and 8 mm and TMEO-6 mm and 8 mm are shown in Table 3. In the gastrocnemius lateralis, the EMG activity significantly increased between the SO and NIRO-8 mm (22.27 ± 2.51 vs. 25.96 ± 4.68 mV; p < 0.05). There was another statistically significant increase between SO and TMEO-6 mm (22.27 ± 2.51 vs. 24.72 ± 5.08 mV, p < 0.05) and vs. TMEO-8 mm (25.49 ± 1.97, p < 0.001), but the low ICC of the last value invalidated the reliability of this value. For the gastrocnemius medialis, a statistically significant increase in the EMG activity was noted for SO vs. NIRO-6 mm (22.93 ± 2.1 vs. 26.44 ± 3.63, p < 0.001), vs. NIRO-8 mm (28.89 ± 3.6, p < 0.001), vs. TMEO-6 mm (25.12 ± 3.51, p < 0.05), and vs. TMEO-8 mm (26.38 ± 3.02, p < 0.05). The latter was not considered because of its low ICC value. In addition, the relationship between NIROs and TMEOs showed that there was a statistically significant increase in NIRO-6 mm and NIRO-8 mm (26.44 ± 3.63 vs. 28.89 ± 3.6, p < 0.05), and a statistically significant decrease in NIRO-8 mm vs. TMEO-6 mm (28.89 ± 3.6 vs. 25.12 ± 3.51, p < 0.001) and in NIRO-8 mm vs. TMEO-8 mm (28.89 ± 3.6 vs. 26.38 ± 3.02, p < 0.05), although the latter could not be considered because of its low ICC values. Sensors 2020, 20, 3205 8 of 12 Table 2. Reliability ICC of variables with “shoe only” versus 6- and 8-mm of novel inverted rocker orthoses (NIRO) and traditional Morton extension orthoses (TMEO). Variables SO NIRO-6 mm NIRO-8 mm TMEO-6 mm TMEO-8 mm ICC (95% CI) MDC ICC (95% CI) MDC ICC (95% CI) MDC ICC (95% CI) MDC ICC (95% CI) MDC SEM 0.950 SEM 0.950 SEM 0.950 SEM 0.950 SEM 0.950 Gastrocnemius lateralis (mV) 0.839 0.932 0.971 0.937 0.458 (0.651–0.935) 1.010 3.560 (0.852–0.973) 1.254 3.477 (0.938–0.988) 0.817 2.264 (0.861–0.975) 1.359 3.766 (0.148–0.777) 2.092 5.798 Gastrocnemius medialis (mV) 0.848 0.832 0.767 0.872 0.894 (0.649–0.94) 0.913 2.530 (0.637–0.931) 1.707 4.731 (0.501–0.905) 2.083 5.775 (0.723–0.948) 1.408 3.904 (0.77–0.957) 0.326 0.904 Abbreviations: ICC = intraclass correlation coefficient; CI = confidence interval; SEM = standard error of measurement; MDC = minimal detectable change; (mV) = millivolts; SO = shoe only; and mm = millimeters. Table 3. Signal amplitudes and comparison values of the mean gastrocnemius lateralis and medialis muscle activities. SO NIRO 6 mm NIRO 8 mm TMEO 6 mm TMEO 8 mm p-Value SO p-Value SO p-Value SO p-Value SO p-Value NIRO 6 mm p-Value NIRO 6 mm p-Value NIRO 6 mm p-Value NIRO 8 mm p-Value NIRO 8 mm p-Value TMEO 6 mm Variable mean (mV) mean(mV) mean (mV) mean (mV) mean (mV) vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. gastrocnemius lateralis ±SD (95% CI) ±SD (95% CI) ±SD (95% CI) ±SD (95% CI) ±SD (95% CI) NIRO 6 mm NIRO 8 mm TMEO 6 mm TMEO 8 mm NIRO 8 mm TMEO 6 mm TMEO 8 mm TMEO 6 mm TMEO 8 mm TMEO 8 mm 22.27 ± 2.51 24.65 ± 4.51 25.96 ± 4.68 24.72 ± 5.08 25.49 ± 1.97 (20.77–23.279) (22.41–26.897) (23.634–28.29) (23.675–27.35) (22.19–27.253) 0.085 <0.05 * <0.05 * <0.001 ** 0.39 0.88 0.356 0.372 0.67 0.913 22.93 ± 2.1 26.44 ± 3.63 28.89 ± 3.6 25.12 ± 3.51 26.38 ± 3.02 gastrocnemius medialis (21.88–23.97) (24.63–28.24) (27–30.68) (23.37–26.87) (24.88–27.89) <0.001 ** <0.001 ** <0.05 * <0.05 * <0.05 * 0.06 0.67 <0.001 ** <0.05 * 0.22 Abbreviations: mV = millivolts; SO = shoe only; NIRO = novel inverted rocker orthoses; TMEO = traditional Morton extension orthoses; mm = millimeters; ±SD = standard deviation; p < 0.05 * (95% CI) was considered statistically significant; and p < 0.001 ** (95% CI) was considered statistically significant. Sensors 2020, 20, 3205 9 of 12 4. Discussion 4.1. TMEO and NIRO Effects This is the first study on EMG muscle activity in the gastrocnemius medialis and lateralis under IMTPJ dorsiflexion mobility restrictions by two different kinds of orthoses, the TMEO and the NIRO, in healthy subjects during running. TMEO has been used to treat symptoms of the first stages of OA [9–11] moving away dorsally from the contact between the proximal phalanx of the hallux and first metatarsal head surfaces. However, it is unclear if the effects on the triceps surae activity that were caused by the windlass mechanism [24] alteration through the IMTPJ caused the restriction. Some authors have shown the need for proper dorsiflexion of the IMTPJ during the push-off phase to ensure normal activity of the calcaneus–plantar system [24]. We hypothesized that TMEO would increase the EMG triceps surae activity that is induced by restriction of IMTPJ dorsiflexion. Our results showed that EMG activity of the gastrocnemius lateralis and medialis increased with TMEO-6 mm and that there is a further increase with TMEO-8 mm compared to SO (Table 3), although the last one could not be considered because of the low ICC values. Even knowing that there are no studies related to EMG activity during running with the orthopedic restriction of IMPTJ dorsiflexion, these results are consistent with other simulated running research [24,25], which showed that engaging the windlass mechanism by promoting 30◦ of IMTPJ dorsiflexion caused the arch to absorb and dissipate more elastic energy than under normal circumstances, and likely the energy of the triceps surae would be saved. In the present research, we decreased the windlass capacity through the TMEO, and followed the lack of storage and release energy in the medial longitudinal arch primary in the heel-off phase; this could have been supported by increasing gastrocnemius musculature EMG activity, as shown by our results, and by sustaining the connection between the IMTPJ and triceps surae through the windlass mechanism, according with other authors [24,25]. We hypothesized that NIRO would produce less EMG activity on triceps surae than the TMEO compared to SO. The rationale behind this approach was that its smooth edges and inverted rocker would produce a slight movement restriction of the IMTPJ; therefore, less effort would be required of the triceps surae to move the heel up. However, the present research showed surprising results, with a higher increase in EMG activity in both the gastrocnemius medialis and lateralis muscles (Table 3) with NIRO compared to TMEO, especially with NIRO-8 mm. This could be partly explained because of the soft edges of the NIRO, which yielded instability on the IMTPJ and transferred it to triceps surae in the heel-off phase. This is consistent with other studies with inverted rocker-sole shoes [40] that showed increased plantarflexion at the ankle joint and an increase in lower limb muscular activity [13]. This conclusion is not consistent with other research that showed increasing toe joint stiffness and increased ankle foot push-off work by up to 181% [41]. 4.2. Osteoarthritis OA has been defined as one of the most important and incapacitating musculoskeletal disorders in the world and OA of the IMTPJ, is the most commonly affected region on the foot [42]. This pathology can involves partial (FHL) or total (HR) rolling fail of the proximal phalanx of the hallux around first metatarsal bone in the last phase of gait [3], and there are a few treatments to relieve them, looking to avoid contact of the dorsal aspect of theses bones, such as TMEO [9–11] or classical rocker soles [12]. No studies about triceps surae EMG activity and IMTPJ OA using orthoses and/or rocker soles during running have been reported; nevertheless, our observations with simulated IMTPJ restriction through TMEO and NIRO, showed an increase of EMG activity pattern of the gastrocnemius medialis and lateralis, in contrast with a recently study [12] with IMTPJ OA and traditional rocker bottom soles, which argued that the reduction of the concentric activity of the triceps surae inferred from the forward displacements of the body center of mass was probably due to passively roll-over of the whole base of support. Sensors 2020, 20, 3205 10 of 12 4.3. Running Economy Elastic energy is stored and returned by the plantar muscles, plantar aponeurosis, and triceps surae with the Achilles tendon during the mid-stance and heel-off phases of running because of its isometric, concentric, and eccentric stretching–shortening pattern [43,44], which shows that the foot has an important role in RE. RE is related to different biomechanical parameters such as shorter ground contact times, higher stride frequency, joint stiffness, and neuromuscular response [20], specifically the pre-activation of gastrocnemius muscular group [14,17,20]. TMEO and NIRO somehow produced decreased stiffness in the IMTPJ by dorsal migration of the I metatarsal bone, and this was shown by the compensatory increase effect on the gastrocnemius musculature activity that attempts to stabilize IMTPJ instability when joined with the windlass mechanism. This would cause worse RE [20]. Our obtained values confirm the results of some studies [45,46], which showed the importance of neuromuscular pre-activation of the gastrocnemius to increase the leg stiffness, anticipating the loading forces and attenuating the effort of the foot to stabilize the joint as required, improving the energy cost and, therefore, the RE. 5. Limitations The sample size that was calculated in a previous study could not be attained because three individuals were excluded. This must be taken into account when interpreting the results. In addition, we were not able to assess the “order effect” on our sample because didn’t write the different orders of each participant’s choice, despite the fact that both groups had a similar participant number, the hypothetical order effect can take over, and we recommended future study designed to improve this aspect of the assessments. Because of the short running test duration when NIRO and TMEO were worn, the hypothetical muscular adaptations of the triceps surae could not be assessed. Longer studies in the future are needed to determine how the exertion levels can influence these muscular adaptations during running. Considering that most ±SD values obtained in the present research are higher than SEM, authors recommended to have caution in interpreting the results. 6. Conclusions NIRO and TMEO have shown a high interaction with triceps surae, increasing the gastrocnemius medialis and lateralis EMG activity during running. This may be additional evidence of the biomechanics relationship between IMTPJ and the windlass mechanism connection. Higher values of the triceps surae EMG activity wearing NIRO and TMEO during running could have a negative impact on RE; therefore, clinicians should be prescribing them with caution when they want to treat IMTPJ OA in runners. Author Contributions: Conceptualization, R.S.-G.; methodology, C.R.-M., M.K. and I.O.-S.; software, I.Z.-G. and I.O.-S.; validation, Á.G.-C. and P.A.; formal analysis, C.R.-M., B.D.-l.-C.-T. and I.Z.-G.; investigation, R.S.-G. and P.A.; resources, C.R.-M. and B.D.-l.-C.-T.; data curation, B.D.-l.-C.-T.; writing—original draft preparation; R.S.-G., C.R.-M., B.D.-l.-C.-T., I.O.-S., I.Z.-G., P.A. and M.K.; visualization, P.A.; supervision, Á.G.-C. and M.K.; project administration, B.D.-l.-C.-T. All authors have read and agreed to the published version of the manuscript Funding: This research received no external fundings. Conflicts of Interest: The authors declare no conflict of interest. References 1. Cotterill, J.M. Stiffness of the Great Toe in Adolescents. Br. Med. J. 1887, 1, 1158. Available online: http://www.ncbi.nlm.nih.gov/pubmed/20751923 (accessed on 21 February 2019). [CrossRef] [PubMed] 2. Laird, P.O. Functional Hallux Limitus. Ill. Podiatr. 1972, 9, 4. 3. Drago, J.J.; Oloff, L.; Jacobs, A.M. A comprehensive review of hallux limitus. J. Foot Surg. 1984, 23, 213–220. [PubMed] Sensors 2020, 20, 3205 11 of 12 4. Zammit, G.V.; Menz, H.B.; Munteanu, S.E. Structural factors associated with hallux limitus/rigidus: A systematic review of case control studies. J. Orthop. Sports Phys. Ther. 2009, 39, 733–742. [CrossRef] [PubMed] 5. Dananberg, H.J. Functional hallux limitus and its relationship to gait efficiency. J. Am. Podiatr. Med. Assoc. 1986, 76, 648–652. Available online: http://www.ncbi.nlm.nih.gov/pubmed/3814239 (accessed on 17 August 2015). [CrossRef] 6. Tenforde, A.S.; Yin, A.; Hunt, K.J. Foot and Ankle Injuries in Runners. Physical Medicine and Rehabilitation Clinics of North America; Saunders, W.B., Ed.; Elsevier Inc.: Amsterdam, The Netherlands, 2016; Volume 27, pp. 121–137. Available online: http://www.ncbi.nlm.nih.gov/pubmed/26616180 (accessed on 15 May 2020). 7. Polzer, H.; Polzer, S.; Brumann, M.; Mutschler, W.; Regauer, M. Hallux rigidus: Joint preserving alternatives to arthrodesis—A review of the literature. World J. Orthop. 2014, 5, 6–13. [CrossRef] 8. Shurnas, P.S. Hallux Rigidus: Etiology, Biomechanics, and Nonoperative Treatment. Foot Ankle Clin. 2009, 14, 1–8. [CrossRef] 9. Dananberg, H.J. Gait style as an etiology to chronic postural pain. Part, I. Functional hallux limitus. J. Am. Podiatr. Med. Assoc. 1993, 83, 433–441. [CrossRef] 10. Smith, R.W.; Katchis, S.D.; Ayson, L.C. Outcomes in hallux rigidus patients treated nonoperatively: A long-term follow-up study. Foot Ankle Int. 2000, 21, 906–913. [CrossRef] 11. Grady, J.F.; Axe, T.M.; Zager, E.J.; Sheldon, L.A. A retrospective analysis of 772 patients with hallux limitus. J. Am. Podiatr. Med. Assoc. 2002, 92, 102–108. [CrossRef] 12. Menz, H.B.; Auhl, M.; Tan, J.M.; Levinger, P.; Roddy, E.; Munteanu, S.E. Biomechanical Effects of Prefabricated Foot Orthoses and Rocker-Sole Footwear in Individuals with First Metatarsophalangeal Joint Osteoarthritis. Arthritis Care Res. 2016, 68, 603–611. [CrossRef] 13. Romkes, J.; Rudmann, C.; Brunner, R. Changes in gait and EMG when walking with the Masai Barefoot Technique. Clin. Biomech. 2006, 21, 75–81. Available online: https://www.sciencedirect.com/science/article/ abs/pii/S0268003305001816 (accessed on 15 May 2020). [CrossRef] [PubMed] 14. Saunders, P.U.; Pyne, D.B.; Telford, R.D.; Hawley, J.A. Reliability and variability of running economy in elite distance runners. Med. Sci. Sports Exerc. 2004, 36, 1972–1976. [CrossRef] [PubMed] 15. Hayes, P.R.; Bowen, S.J.; Davies, E.J. The relationships between local muscular endurance and kinematic changes during a run to exhaustion at vVO2MAX. J. Strength Cond. Res. 2004, 18, 898–903. 16. Roca-Dols, A.; Losa-Iglesias, M.E.; Sánchez-Gómez, R.; Becerro-de-Bengoa-Vallejo, R.; López-López, D.; Rodríguez-Sanz, D.; Martínez-Jiménez, E.M.; Calvo-Lobo, C. Effect of the cushioning running shoes in ground contact time of phases of gait. J. Mech. Behav. Biomed. Mater. 2018, 88, 196–200. [CrossRef] [PubMed] 17. Roca-Dols, A.; Elena Losa-Iglesias, M.; Sánchez-Gómez, R.; Becerro-de-Bengoa-Vallejo, R.; López-López, D.; Palomo-López, P.; Rodríguez-Sanz, D.; Calvo-Lobo, C. Electromyography activity of triceps surae and tibialis anterior muscles related to various sports shoes. J. Mech. Behav. Biomed. Mater. 2018, 86, 158–171. [CrossRef] 18. Roca-Dols, A.; Losa-Iglesias, M.E.; Sánchez-Gómez, R.; López-López, D.; Becerro-de-Bengoa-Vallejo, R.; Calvo-Lobo, C. Electromyography comparison of the effects of various footwear in the activity patterns of the peroneus longus and brevis muscles. J. Mech. Behav. Biomed. Mater. 2018, 82, 126–132. [CrossRef] 19. Blagrove, R.C.; Howatson, G.; Hayes, P.R. Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review. Sports Medicine; Springer International Publishing: Cham, Switzerland, 2018; Volume 48, pp. 1117–1149. 20. Tam, N.; Tucker, R.; Santos-Concejero, J.; Prins, D.; Lamberts, R.P. Running Economy: Neuromuscular and Joint-Stiffness Contributions in Trained Runners. Int. J. Sports Physiol. Perform. 2019, 14, 16–22. [CrossRef] 21. Hicks, J. The mechanics of the foot. II. The plantar aponeurosis and the arch. J. Anat. 1954, 88, 25–30. 22. Stearne, S.M.; McDonald, K.A.; Alderson, J.A.; North, I.; Oxnard, C.E.; Rubenson, J. The Foot’s Arch and the Energetics of Human Locomotion. Sci. Rep. 2016, 6, 1–10. [CrossRef] 23. Ker, R.F.; Bennett, M.B.; Bibby, S.R.; Kester, R.C.; Alexander, R.M. The spring in the arch of the human foot. Nature 1987, 325, 147–149. [CrossRef] 24. Maceira, E.; Monteagudo, M. Functional hallux rigidus and the Achilles-calcaneus-plantar system. Foot Ankle Clin. 2014, 19, 669–699. [CrossRef] 25. Welte, L.; Kelly, L.A.; Lichtwark, G.A.; Rainbow, M.J. Influence of the windlass mechanism on arch-spring mechanics during dynamic foot arch deformation. J. R. Soc. Interface 2018, 15, 20180270. [CrossRef] Sensors 2020, 20, 3205 12 of 12 26. Johanson, M.A.; Allen, J.C.; Matsumoto, M.; Ueda, Y.; Wilcher, K.M. Effect of Heel Lifts on Plantarflexor and Dorsiflexor Activity During Gait. Foot Ankle Int. 2010, 31, 1014–1020. [CrossRef] [PubMed] 27. Von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. Int. J. Surg. 2014, 12, 1495–1499. [CrossRef] [PubMed] 28. Redmond, A.C.; Crosbie, J.; Ouvrier, R.A. Development and validation of a novel rating system for scoring standing foot posture: The Foot Posture Index. Clin. Biomech. (Bristol. Avon.) 2006, 21, 89–98. [CrossRef] 29. Root, M.L.; Orien, W.P.; Weed, J.H.; Hughes, R.J. Normal and Abnormal Function of the Foot; Clinical Biomechanics Corp.: Los Angeles, CA, USA, 1977; Volume 2. 30. Sánchez-Gómez, R.; Becerro-de-Bengoa-Vallejo, R.; Losa-Iglesias, M.E.; Calvo-Lobo, C.; Navarro-Flores, E.; Palomo-López, P.; Romero-Morales, C.; López-López, D. Reliability Study of Diagnostic Tests for Functional Hallux Limitus. Foot Ankle Int. 2020, 41, 457–462. [CrossRef] [PubMed] 31. Garrow, J.S.; Webster, J. Quetelet’s index (W/H2) as a measure of fatness. Int. J. Obes. 1985, 9, 147–153. [PubMed] 32. Naess, I.; Bø, K. Can maximal voluntary pelvic floor muscle contraction reduce vaginal resting pressure and resting EMG activity? Int. Urogynecol. J. 2018, 29, 1623–1627. [CrossRef] 33. Hermens, H.J.; Freriks, B.; Disselhorst-Klug, C.; Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. J. Electromyogr. Kinesiol. 2000, 10, 361–374. [CrossRef] 34. Murley, G.S.; Buldt, A.K.; Trump, P.J.; Wickham, J.B. Tibialis posterior EMG activity during barefoot walking in people with neutral foot posture. J. Electromyogr. Kinesiol. 2009, 19, e69–e77. [CrossRef] [PubMed] 35. Shih, Y.; Lin., K.-L.; Shiang, T.-Y. Is the foot striking pattern more important than barefoot or shod conditions in running? Gait Posture 2013, 38, 490–494. [CrossRef] [PubMed] 36. Goto, K.; Abe, K. Gait characteristics in women’s safety shoes. Appl. Ergon. 2017, 65, 163–167. [CrossRef] 37. Fleming, N.; Walters, J.; Grounds, J.; Fife, L.; Finch, A. Acute response to barefoot running in habitually shod males. Hum. Mov. Sci. 2015, 42, 27–37. [CrossRef] [PubMed] 38. Landis, J.R.; Koch, G.G. The measurement of observer agreement for categorical data. Biometrics 1977, 33, 159–174. [CrossRef] [PubMed] 39. Jacobson, N.; Truax, P. Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. J. Consult Clin. Psychol. 1991, 59, 12–19. [CrossRef] 40. Myers, K.A.; Long, J.T.; Klein, J.P.; Wertsch, J.J.; Janisse, D.; Harris, G.F. Biomechanical implications of the negative heel rocker sole shoe: Gait kinematics and kinetics. Gait Posture 2006, 24, 323–330. [CrossRef] 41. Honert, E.C.; Bastas, G.; Zelik, K.E. Effect of toe joint stiffness and toe shape on walking biomechanics. Bioinspir. Biomim. 2018, 13, 066007. [CrossRef] 42. Sebbag, E.; Felten, R.; Sagez, F.; Sibilia, J.; Devilliers, H.; Arnaud, L. The world-wide burden of musculoskeletal diseases: A systematic analysis of the World Health Organization Burden of Diseases Database. Ann. Rheum. Dis. 2019, 78, 844–848. [CrossRef] 43. Biewener, A.A.; Roberts, T.J. Muscle and tendon contributions to force, work, and elastic energy savings: A comparative perspective. Exerc. Sport Sci. Rev. 2000, 28, 99–107. 44. Wilson, A.; Lichtwark, G. The anatomical arrangement of muscle and tendon enhances limb versatility and locomotor performance. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2011, 366, 1540–1553. [CrossRef] [PubMed] 45. Boyer, K.A.; Nigg, B.M. Muscle activity in the leg is tuned in response to impact force characteristics. J. Biomech. 2004, 37, 1583–1588. [CrossRef] [PubMed] 46. Hamner, S.R.; Seth, A.; Delp, S.L. Muscle contributions to propulsion and support during running. J. Biomech. 2010, 43, 2709–2716. [CrossRef] [PubMed] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). (...TRUNCATED)

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